The customer introduced themselves with an interesting problem of analysing CCTV video recordings of traffic videos on roadways. They wanted to leverage the CCTV video recordings and generate insightful analytics about the traffic on residential and commercial properties. The challenge was to generate the results of the video analytics in a time-efficient and a power-efficient manner.
We developed a Deep Neural Network model to recognise six categories of vehicles namely cars, motorcycles, buses, trucks, bicycles and pedestrians. We first collected the CCTV footage and generated a customised data set containing 2000 images for each class of vehicle. We then trained a Deep Neural Network to classify each class of vehicle and localize them on a video frame. Since this was a video processing application, performance was a key metric in addition to the accuracy of the vehicle finder.
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