Vehicle Detection and Tracking

In this vehicle detection and tracking project, we detect in a video pipeline, potential boxes, via a sliding window, that may contain a vehicle by using a Support Vector Machine Classifier for prediction to create a heat map. The heat map history is then used to filter out false positives before identification of vehicles by drawing a bounding box around it.

Vehicle Detection Sample
Vehicle Detection Sample

Vehicle Detection Project

The goals / steps of this project are the following:

  • Perform a Histogram of Oriented Gradients (HOG) feature extraction on a labeled training set of images and train a classifier Linear SVM classifier
  • Optionally, you can also apply a color transform and append binned color features, as well as histograms of color, to your HOG feature vector.
  • Note: for those first two steps don’t forget to normalize your features and randomize a selection for training and testing.
  • Implement a sliding-window technique and use your trained classifier to search for vehicles in images.
  • Run your pipeline on a video stream (start with the test_video.mp4 and later implement on full project_video.mp4) and create a heat map of recurring detections frame by frame to reject outliers and follow detected vehicles.
  • Estimate a bounding box for vehicles detected.

A jupyter/iPython data science notebook was used and can be found on github Full Project RepoVehicle Detection Project Notebook (Note the interactive ipywidgets are not functional on github). As the notebook got rather large I extracted some code into python files (functions to extract, loading helpers), (feature extraction and classes), (image and window slice processing), (holds search parameters class), (windowing and box classes) and (main VehicleDetection class that coordinates processing of images). The project is written in python and utilises numpy, OpenCV, scikit learn and MoviePy.

Histogram of Oriented Gradients (HOG)

Through a bit of trial and error I found a set of HOG parameters.

HOG Feature Extraction and Parameters

A function extract_hog_features was created that took an array of 64x64x3 images and returned a set of features. These are extracted in parallel and it in turn uses HogImageFeatures class.

As the hog algorithm is primarily focused on grey images, I initially used the YCrCB colour space with the Y channel (used to represent a gray images). However I found that it was not selective enough during the detection phase. I thus used all 3 colour channels. To reduce the number of features, I increased the number of HOG pixels per cell. I used an interactive feature in my notebook to find an orient setting of 32 that showed distinctive features of vehicle. Sample follows.

Training Vehicle HOG Sample
Training Vehicle HOG Sample

The final parameter settings used color_space = 'YCrCb',orient = 32,pix_per_cell = 16 and hog_channel = 'ALL'. Experimentation occurred with using Colour Histogram Features but it slowed down feature extraction and later increased the number of false positives detected. Per the following visualisation graphic, you can see that the Cr and Cb colour spaces had detectable hog features

Sample HOG Channel Output form a video window slice
Sample HOG Channel Output form a video window slice

Classifier Training

Once HOG features (no Colour Hist or Bin Spatial) were extracted from car (GTI Vehicle Image Database and Udacity Extras) and not_car (GTI, KITTI) image sets. They were then stacked and converted to float in the vehicle detection notebook.

Features were then scaled using the Sklearn RobustScaler sample result follows.
RobustScaler Feature Sample

Experimentation occurred in the Classifier Experimentation Notebook between LinearSVC (Support Vector Machine Classifier), RandomForest and ExtraTrees classifiers. LinearSVC was chosen as the prediction time was 0.00228 seconds for 10 labels compared to ~0.10 seconds for the other two.

Sliding Window Search

Building sliding windows

For this project four sizes of windows were chosen – 32×32, 48×48, 64×64 and 128×128 and position at different depth perspective on the bottom right side of the image to cover the road. The larger windows closer to the driver and the smaller closer to the horizon. Overlap in both x,y was set between 0.5 and 0.8 to balance the need for better coverage vs number of boxes generated – currently 937. The more boxes for a sliding window, the more calculations per video image.
Window Search Example

Classifier examples and optimisation

Some time was spent on parallelisation of the search using Python async methods and asyncio.gather in the VehicleDetection class. The search extracts the bounded box image of each sized search window and scales it to 64×64 before doing feature extraction and prediction on each window.
Small Window Slice Scaled to 64x64

The search hot_box_search returns an array of hot boxes that classifier has predicted contains a vehicle.

These boxes overlap and are used to create a clipped at 255, two dimensional heat map. To remove initial false positives counts > 4 are kept. The heat map is then normalised before another threshold is applied

heatmap = apply_threshold(heatmap, 4)
heatmap_std = heatmap.std(ddof=1)
if heatmap_std != 0.0:
    heatmap = (heatmap-heatmap.mean())/heatmap_std
heatmap = apply_threshold(heatmap, np.max([heatmap.std(), 1]))    

Plotting this stage back onto the image
detected boxes and heatmap

A history is kept of heat maps Heatmap History which is then used as input into Scipy Label with a dim binary structure linking dimensions, giving
Heatmap with corresponding 2 cars identified labels
finally a variance filter is applied on each box, if for one detected label boxes are ignored with a variance < 0.1 (its just a few close points0 or if multiple with a variance < 1.5 (more noise).

Video Implementation

Vehicle Detection Video

The Project VehicleDetection mp4 on GitHub, contains the result (YouTube Copy)

Result Video embedded from YouTube

Tracking Vehicle Detections

One of the nice features of the scipy.ndimage.measurements.label function is that it can process 3d arrays giving labels in x,y,z spaces. Thus when using the array of heat map history as input, it labels connections in x,y,z. If a returned label box is not represented in at least 3 (heat map history max – 2) z planes then it is rejected as a false positive. The result is that a vehicle is tracked over the heat map history kept.


When construction this pipeline, I spent some time working on parallelising the window search. What I found is that there is most likely little overall performance improvement to be gained by doing so. Images have to be processed in series and whilst generating the video, my cpu was under utilised.

In hindsight I should of used a heavy weight search to detect vehicles and then a more lighter weight, narrower search primed by the last known positions. Heavy weight searching could be run at larger intervals or when a vehicle detection is lost.

My pipeline would fail presently if vehicles were on the left hand side or centre of the car. I suspect trucks, motorbikes, cyclists and pedestrians would not be detected (as they are not in the training data).

Advanced Lane Detection

In this Advanced Lane Detection project, we apply computer vision techniques to augment video output with a detected road lane, road radius curvature and road centre offset. The video was supplied by Udacity and captured using the middle camera.

sample lane detection result
sample lane detection result

The goals / steps of this project are the following:

  • Compute the camera calibration matrix and distortion coefficients given a set of chessboard images.
  • Apply a distortion correction to raw images.
  • Use color transforms, gradients, etc., to create a thresholded binary image.
  • Apply a perspective transform to rectify binary image (“birds-eye view”).
  • Detect lane pixels and fit to find the lane boundary.
  • Determine the curvature of the lane and vehicle position with respect to center.
  • Warp the detected lane boundaries back onto the original image.
  • Output visual display of the lane boundaries and numerical estimation of lane curvature and vehicle position.

A jupyter/iPython data science notebook was used and can be found on github Full Project RepoAdvanced Lane Finding Project Notebook (Note the interactive ipywidgets are not functional on github). The project is written in python and utilises numpy and OpenCV.

Camera Calibration

Every camera has some distortion factor in its lens. The known approach to correct for that in (x,y,z) space is apply coefficients to undistort the image. To calculate this a camera calibration process is required.

It involves reading a set of warped chessboard images, converting them into grey scale images before using cv2.findChessboardCorners() to identify the corners as imgpoints.
9x6 Chessboard Corners Detected

If corners are detected then they are collected as image points imgpoints along with a set of object points objpoints; with an assumption made that the chessboard is fixed on the (x,y) plane at z=0 (object points will hence be the same for each calibration image).

In the function camera_calibrate I pass the collected objpoints, imgpoints and a test image for the camera image dimensions. It in turn uses cv2.calibrateCamera() to calculate the distortion coefficients before the test image is undistorted with cv2.undistort() giving the following result.
Original and Undistorted image

Pipeline (Test images)

After camera calibration a set of functions have been created to work on test images before later being used in a video pipeline.

Distortion corrected image

The undistort_image takes an image and defaults the mtx and dist variables from the previous camera calibration before returning the undistorted image.
test image distorted and undistorted

Threshold binary images

A threshold binary image, as the name infers, contains a representation of the original image but in binary 0,1 as opposed to a BGR (Blue, Green, Red) colour spectrum. The threshold part means that say the Red colour channel( with a range of 0-255) was between a threshold value range of 170-255, that it would be set to 1.

A sample output follows.
Sample Threshold Image

Initial experimentation occurred in a separate notebook before being refactored back into the project notebook in the combined_threshold function. It has a number of default thresholds for sobel gradient x&y, sobel magnitude, sober direction, Saturation (from HLS), Red (from RGB) and Y (luminance from YUV) plus a threshold type parameter (daytime-normal, daytime-bright, daytime-shadow, daytime-filter-pavement).

Whilst the daytime-normal threshold worked great for the majority of images there were situations where it didn’t e.g. pavement colour changes in bright light and shadow.

Daytime Normal with noise bright light & pavement change
Daytime Normal with noise bright light & pavement change
Daytime Normal with shadow
Daytime Normal with shadow

Other samples Daytime Bright, Daytime Shadow and Daytime Filter Pavement.

Perspective transform – birds eye view

To be able to detect the road lines, the undistorted image is warped. The function calc_warp_points takes an image’s height & width and then calculates the src and dst array of points. perspective_transforms takes them and returns two matrixes M and Minv for perspective_warp and perpective_unwarp functions respectively. The following image, shows an undistorted image, with the src points drawn with the corresponding warped image (the goal here was straight lines) Distorted with bird's eye view

Lane-line pixel identification and polynomial fit

Once we have a birds eye view with a combined threshold we are in a position to identify lines and a polynomial to draw a line (or to search for points in a binary image).

topdown warped binary image
topdown warped binary image

A histogram is created via lane_histogram from the bottom third of the topdown warped binary image. Within lane_peaks, scipy.signal is used to identify left and right peaks. If just one peak then the max bin either side of centre is returned.

calc_lane_windows uses these peaks along with a binary image to initialise a left and right instance of a WindowBox class. find_lane_window then controls the WindowBox search up the image to return an array of WindowBoxes that should contain the lane line. calc_fit_from_boxes returns a polynomial or None if nothing found.

poly_fitx function takes a fity where
fity = np.linspace(0, height-1, height) and a polynomial to calculate an array of x values.

The search result is plotted on the bottom left of the below image with each box in green. To test line searching by polynomial, I then use the left & right WindowBox search polynomials as input to calc_lr_fit_from_polys. The bottom right graphic has the new polynomial line draw with a blue search window (relates to polynomial used for the search from WindBoxes) that was used overlapping with a green window for the new.

Warped box seek and new polynomial fit
Warped box seek and new polynomial fit

Radius of curvature calculation and vehicle from centre offset

In road design, curvature is important and its normally measured by its radius length. For a straight line road, that value can be quite high.

In this project our images are in pixel space and need to be converted into meters. The images are of US roads and I measured from this image the distance between lines (413 pix) and the height of dashes (275 px). Lane width in the US is ~ 3.7 meters and dashed lines 3 metres. Thus xm_per_pix = 3.7/413 and ym_per_pix = 3./275 were used in calc_curvature. The function converted the polynomial from pixel space into a polynomial in meters.

To calculate the offset from centre, I first determined where on the x plane, both the left lx and right rx lines crossed the image near the driver. I then calculated the xcentre of the image as the width/2. The offset was calculated such (rx - xcenter) - (xcenter - lx) before being multiple by xm_per_pix.

Final pipeline

I decided to take a more python class based approach once I progressed through this project. Inside the classes, I called the functions mentioned previously. The classes created were:

  • Lane contains image processing, final calculations for view drawing and reference to left and right RoadLines. It also handled searching for initial lines, recalculations and reprocessing a line that was not sane;
  • RoadLine contains a history of Lines and associated curvature and plotting calculations using weighted means; and
  • Line contains detailed about the line and helper functions

Processing is triggered by setting the Lane.image variable. Convenient property methods Lane.warped, Lane.warped_decorated, lane.result and lane.result_decorated return processed images. It made it very easy to debug output using interactive ipywidgets (which don’t work on github)

Sample result images


Pipeline (Video)

Using moviepy to process the project video was simple. I also decorated the result with a frame count. The Project Video Lane mp4 on GitHub, contains the result (YouTube Copy)


Problems/Issues faced

To some degree, I got distracted with trying to solve the issues I found in my algorithm with the challenge videos. This highlighted, that I need to improve my understanding of colour spaces, sobel and threshold combinations.

I included a basic algorithm to remove pavement colours from the images using a centre, left and right focal point. I noticed that the dust colour on the vehicle seemed to be also in the road side foliage. This however wasn’t sufficient to remove all pavement colour and didn’t work when there was a road type transition. It was very CPU intensive.

In the end, I used a combination of different methods, that used a basic noise filter on warped binary images to determine, if it was sufficient to look for a line or not. If it wasn’t it tried the next one, with the final being a vertical rectangle window crawl down the image. Where the best filter was determined for each box. Again this was CPU intensive, but worked.

Another issue faced was using the previous curvature radius to determine if this line was sane or not. The values were too jittery and when driving on a straight line, high. I decided not to pursue this.

Opportunities for improvement in the algorithm/pipeline

There is room here for some refactoring into a more Object oriented approach. This was not evident at the start of the project as to how it should be structured. I experimented a little with using Pool from multiprocessing to parallelise left and right lane searches. It didn’t make it into my final classes as for normal line searching using a polynomial, as I did not ascertain if the multiprocessing overhead, outweighed the parallelism value. Certainly potential here to use a more functional approach to give the best runtime options for parallelisation.

Other areas, include automatically detecting the src points for warp, handling bounce in the road and understanding surface height (above road) of the camera and its impact.

I thought also as I’ve kept history, I could extend the warp to include a bird’e eye representation of the car on the road and directly behind it. I did mean averaging on results for smoothing drawn lines, but this was not included in the new line calculations from the next image frames.

The algorithm could also be made to make predictions about the line when there is gaps. This would be easier with continuous lines then dashed.

Hypothetical pipeline failure cases

Pavement fixes and/or combined with other surfaces that create vertical lines near existing road lines.

It would also fail if there was a road crossing or a need to cross lanes or to exit the freeway.

Rain and snow would also have an impact and I’m not sure about night time.

Tail gating a car or a car on a tighter curve would potentially interrupt the visible camera and hence line detection.

Clone Driving Behaviour

Clone driving behaviour using Deep Learning

With this behaviour cloning project, we give steering & throttle instruction to a vehicle in a simulator based on receiving a centre camera image and telemetry data. The steering angle data is a prediction for a neural network model trained against data saved from track runs I performed.
simulator screen sot

The training of the neural net model, is achieved with driving behaviour data captured, in training mode, within the simulator itself. Additional preprocessing occurs as part of batch generation of data for the neural net training.

Model Architecture

I decided to as closely as possible use the Nvidia’s End to End Learning for Self-Driving Cars model. I diverged by passing cropped camera images as RGB, and not YUV, with adjusting brightness and by using the steering angle as is. I experimented with using 1/r (inverse turning radius) as input but found the values were too small (I also did not know the steering ratio and wheel base of the vehicle in the simulator).

Additional experimentation occurred with using, Steering angle prediction model but the number of parameters was higher then the nvidia model and it worked off of full sized camera images. As training time was significantly higher, and initial iterations created an interesting off road driving experience in the simulator, I discontinued these endeavours.

The model represented here is my implementation of the nvidia model mentioned previously. It is coded in python using keras (with tensor flow) in and returned from the build_nvidia_model method. The complete project is on github here Udacity Behaviour Cloning Project


The input is 66x200xC with C = 3 RGB color channels.


Layer 0: Normalisation to range -1, 1 (1./127.5 -1)

Layer 1: Convolution with strides=(2,2), valid padding, kernel 5×5 and output shape 31x98x24, with elu activation and dropout

Layer 2: Convolution with strides=(2,2), valid padding, kernel 5×5 and output shape 14x47x36, with elu activation and dropout

Layer 3: Convolution with strides=(2,2), valid padding, kernel 5×5 and output shape 5x22x48, with elu activation and dropout

Layer 4: Convolution with strides=(1,1), valid padding, kernel 3×3 and output shape 3x20x64, with elu activation and dropout

Layer 5: Convolution with strides=(1,1), valid padding, kernel 3×3 and output shape 1x18x64, with elu activation and dropout

flatten 1152 output

Layer 6: Fully Connected with 100 outputs and dropout

Layer 7: Fully Connected with 50 outputs and dropout

Layer 8: Fully Connected with 10 outputs and dropout

dropout was set aggressively on each layer at .25 to avoid overtraining


Layer Fully Connected with 1 output value for the steering angle.


Keras output plot (not the nicest visuals)

Data preprocessing and Augmentation

The simulator captures data into a csv log file which references left, centre and right captured images within a sub directory. Telemetry data for steering, throttle, brake and speed is also contained in the log. Only steering was used in this project.

My initial investigation and analysis was performed in a Jupyter Notebook here.

Before being fed into the model, the images are cropped to 66×200 starting at height 60 with width centered – A sample video of a run cropped.

Cropped left, centre and right camera image
Cropped left, centre and right camera image

As seen in the following histogram a significant proportion of the data is for driving straight and its lopsided to left turns (being a negative steering angle is left) when using data generated following my conservative driving laps.
Steering Angle Histogram

The log file was preprocessed to remove contiguous rows with a history of >5 records, with a 0.0 steering angle. This was the only preprocessing done outside of the batch generators used in training (random rows are augmented/jittered for each batch at model training time).

A left, centre or right camera was selected randomly for each row, with .25 angle ( for left and – for right) applied to the steering.

Jittering was applied per Vivek Yadav’s post to augment data. Images were randomly transformed in the x range by 100 pixels and in the y range by 10 pixels with 0.4 per xpixel adjusted against the steering angle. Brightness via a HSV (V channel) transform (.25 a random number in range 0 to 1) was also performed.
jittered image

During batch generation, to compensate for the left turning, 50% of images were flipped (including reversing steering angle) if the absolute steering angle was > .1.

Finally images are cropped per above before being batched.

Model Training

Data was captured from the simulator. I drove conservatively around the track three times paying particular attention to the sharp right turn. I found connecting a PS3 controller allowed finer control then using the keyboard. At least once I waited till the last moment before taking the turn. This seems to have stopped the car ending up in the lake. Its also helped to overcome a symptom of the bias in the training data towards left turns. To further offset this risk, I validated the training using a test set I’d captured from the second track, which is a lot more windy.

Training sample captured of left, centre and right cameras cropped

Center camera has the steering angle and 1/r values displayed.

Validation sample captured of left, centre and right cameras cropped

Center camera has the steering angle and 1/r values displayed.

The Adam Optimizer was used with a mean squared error loss. A number of hyper-parameters were passed on the command line. The command I used looks such for a batch size of 500, 10 epochs (dropped out early if loss wasn’t improving), dropout at .25 with a training size of 50000 randomly augmented features with adjusted labels and 2000 random features & labels used for validation

python --batch_size=500 --training_log_path=./data --validation_log_path=./datat2 --epochs 10 \
--training_size 50000 --validation_size 2000 --dropout .25

Model Testing

To meet requirements, and hence pass the assignment, the vehicle has to drive around the first track staying on the road and not going up on the curb.

The model trained (which is saved), is used again in testing. The simulator feeds you the centre camera image, along with steering and throttle telemetry. In response you have to return the new steering angle and throttle values. I hard coded the throttle to .35. The image was cropped, the same as for training, then fed into the model for prediction giving the steering angle.

steering_angle = float(model.predict(transformed_image_array, batch_size=1))
throttle = 0.35

Successful run track 1

Successful run track 1

Successful run track 2

Successful run track 2

note: the trained model I used for the track 1 run, is different to the one used to run the simulator in track 2. I found that the data I originally used to train a model to run both tracks, would occasionally meander on track 1 quite wildly. Thus used training data to make it more conservative to meet requirements for the projects.

My first lane detection algo

I’m all for practical learning by building things. There’s nothing like getting stuck into a project and seeing results. Whilst a little progress is a good motivator, it also shows you how much it is that you don’t know.

I was pleased with the results of my first project doing the Udacity Self Driving Car Engineer Nanodegree. Yet what was more pleasing was being shown how much experimentation is really required. That is that is so much to learn.

This first module was about understanding some of the principles of computer vision that apply. We first started with Canny Edge Detection

and then Hough transform to detect lines within a region of interest.

The first project was to apply this learning, first to set of static images and then to a couple of videos captured whilst driving on a highway.

How cool is the final result .

I submitted my Jupyter Notebook for review. To pass you need to ensure that you meet specification. I passed here is the review feedback.

Some of my reflection thoughts on what can be improved in a future iteration of the project:

  • look for the road horizon starting from bottom centre of the image working up – asphalt has a fairly unique colour
  • break ROI into left and right lanes earlier – seems that at least with driving on a highway without lane changing that we can assume with confidence where they should start at the base of the image
  • segment each ROI into chained vertical blocks of a smaller width
  • when drawing connect intersection of cv2.fitLine lines
  • increase number of segments if lanes are curving left or right
  • label the lines with colour and type – continuous, dashed etc
  • feed the previous result into the evaluation of the next image
  • determine when an image has no lanes that could be considered reasonable
  • lane changing and entering a lane from a curb needs more though
  • if using smaller more specific left and right lane ROIs should allow for following a vehicle
  • not sure how rain affects this – might have to do a test and capture video in a tropical down pour this storm season
  • this approach wouldn’t work in snow. would require a different approach

Its still early yet in the nano degree but I’m hooked already. Happy coding and driving.

Congratulations – You’ve been Accepted

Congratulations – You’ve been Accepted

It was my birthday and a little email arrived from Udacity titled “Congratulations, you’re going to be one of our very first Self-Driving Car students!”. I had never thought with the number of people that were applying that I’d have a chance. Yet here was the email as a birthday present. I was both thrilled and apprehensive – I’d gotten in and now had to follow through.

I’ve been fascinated with robotics and AI for quite sometime now. And as always I seem to be too early with investigating business endeavours in Australia related to such. Whilst my cycling project was interesting, I was getting bogged down with having to learn how to use all the technology by myself. It always surprised me that I was able to get some results given how large the US teams seems to be that were using the same open source projects.

I’d applied to the program, because I thought I could leverage what I’d been learning through my previous endeavours around learnings from my cycling big data sensor project. The course had a focus on using things like ROS (Robotic Operating System), TensorFlow and Open Computer Vision. Plus it’d just seemed to be a great idea to work on technology related to self driving vehicles in a structured way. There is so much interest in this subject matter.

When I shared with my Facebook friends, that I’d been accepted, it was the most liked post I think I’ve ever had. Speaking people they say “wow thats great”. So its past the litmus test and I’ve just started.

The rise of chat bots and the fall of apps

It once was cool to build a mobile app and many startups in the past had built successful businesses with them and a minimalist web site. Now the chances of a new mobile app, creating mindshare and enabling a spot on a person’s home screen is next to impossible. We’ve reached peak app and new style of apps called chat bots are taking mindshare.

App stores are flooded, the majority of apps are rarely downloaded or found for that matter, as they do not rank.

It’s now harder than ever for a developer to build an app, that will replace the staple set of apps, a user does have on their devices. The frontier has changed to chat apps that have a conversational style interface either using text or voice (think siri). If you are building a new mobile app, stop! and reconsider how you are going to reach your target audience.

These new chat apps are leveraging existing instant messaging apps and agents on websites. Increasingly also APIs are being created and exposed to allow developers to interact with well known personal assistants like Siri. Some may argue that the interaction between human and computer is frustrating. I’d agree, having occasional back and forth sessions with Siri, to dial on my iPhone, a person I call regularly. However the situation is slowing improving as machine learning/AI technology improves behind the scenes.

Many will argue that we are not seeing anything new, that it is just the same technology and approaches that have been around for ages. The quest, as such to pass the Turing test where a judge can not determine if he or she, is talking to a machine or a person.

I think we’ve reached an inflection point, where a new class of conversation chat bot is being enabled by the gradual and constant exponential evolution of computing technology, sharing of open source component technology (such as natural language processing) in conjunction with the ongoing to quest to provide individually tailored answers to people’s own question through understanding the explosion of data available online.

This is also backed up by a dramatic increase in tech news coverage regarding startups in the US and with training/conferences covering this area.

So forget building a mobile app and start building a chat bot!



Switching off from Aussie innovation for the time being …

The slow dawn of reality has crept into my thinking, that what I’m presently witnessing, is the rise of politically correct innovation within Australia. That is, that there is a rush on, to be positioned, to secure funding and “innovation wash” existing service offerings, ready for when government programs come into affect.

My high hopes for an ideas boom have been dashed somewhat of late. Not so much from the intent, but from the reality, that the intent does not match reality. There is significant education (dare I say re-education) required.

Let me show you what I mean.

If we look at the innovation website (their definition here ), it basically suggests, that innovation is about change. It follows:

What is innovation?

Innovation generally refers to changing or creating more effective processes, products and ideas, and can increase the likelihood of a business succeeding. Businesses that innovate create more efficient work processes and have better productivity and performance.

Now if we look at the wikipedia innovation article  it suggests, The term “innovation” can be defined as something original and more effective and, as a consequence, new, that “breaks into” the market or society.

Innovation is a new idea, or more-effective device or process.[1] Innovation can be viewed as the application of better solutions that meet new requirements, unarticulated needs, or existing market needs.[2] This is accomplished through more-effective products, processes, services, technologies, or business models that are readily available to markets, governments and society.

So from my perspective the first one, is inwards looking, about the term innovation (as in thats an innovative idea) to do business change or continuous improvement.

I’ve often argued that changing a business process to make it more effective is not innovation. However if that idea, is bought to market, as a new product or services offering, then it is innovation ie there has to be diffusion into a market or society.

Now if we look at the slick new marketing or education material (your viewpoints may differ on this) being produced by National Innovation & Science Agenda Australian Government it refers to how Australians have been good at Ideas, but now we need to get better at commercialising –  turning those ideas, into new products or services.

As you can see, there is a long road ahead, with a lot of jargon presently such as “Ideas Boom”. It will take some time for people, to agree on what things mean (even though they have great definitions available now) and to reach consensus. Then decisions will need to be made about how much capital is to be made available and under what investment thesis it will be allocated.

There are a lot of people shouting about a number of things surrounding these topics and if your not shouting the politically correct message too then no matter how novel and disruptive your idea or invention is, it may not benefit from the “Ideas Boom”. If this is effecting you, jump on a plane and go to Silicon Valley or elsewhere that may be appropriate.

I keep hearing about the lack of opportunity here in Australia, in many fields on podcasts I listen to occasionally. On those podcasts, when people ask eminent panel members about their thoughts on the subject, invariably, their answer will be that we do hope you stay and help drive the next generation. It always surprises me, assuming that these persons are in tenured positioned, how devoid their responses seem to be, from the reality of needing money (or some may say capital) and support to do so. Its this later bit, that’ll take so long to grow here in Australia. It may also require a generational change. The notion of not taking risks in some is the antithesis of what is required in an “ideas boom” era.

So I’m thinking of slowly fading away from observing and commenting on all of this, until I need something concrete from it. Presently it all seems to be a nice discussion, but discussion is after all discussion and not tangible outcomes.