This week I mainly worked on experimenting with the Video Processing methods on the traffic intersection data. I am working with the sherbrooke intersection video data in Montreal.
The first thing I did was image differentiation. Essentially you take one frame and subtract it from the following frame. Theoretically, the only differences should be movement from any moving objects- either vehicles of pedestrians. We then apply a thresholding so that only these moving regions are take into considerations going forward in the pipeline. The results looked a little iffy, they certainly needed to be filtered to get a smoother looking object shape. I have been following the advice in this link: https://www.analyticsvidhya.com/blog/2020/04/vehicle-detection-opencv-python/
The idea is to get this preprocessing as good as possible at outlining the boundaries of the vehicles so that they can be classified correctly by the neural network moving forward. It also may be possible to do this without using a neural network, and this may be a route to check out. For example, if we use an intersection where there are no pedestrians- or where the angle is such that it is very obvious and easy to differentiate between a vehicle and a pedestrian- then there may be a good deterministic approach to deciding that a object’s outline is indeed a vehicle.
I am also presenting this week, so I spent some time polishing up the slides we worked on and practice my presenting skills.