Real time Vehicle Color Recognition on a budget: an investigation on the usage of CNN architectures

In this work, we consider the problem of vehicle color recognition and target scenarios with limited computational resources. Indeed, in real traffic monitoring systems running on the field, algorithms must be light in terms of inference time and memory, but also accurate and robust to the scene variability. We employ end-to-end Convolutional Neural Networks to investigate under which conditions the use of such methodologies- that are state-of-the-art in a multitude of vision-based tasks but often lead to a significant computational burden- can provide us a good compromise between efficiency and effectiveness. We reason on the structure and size of the networks, while monitoring the performance in terms of color classification accuracy and computational effort. We provide an extensive experimental analysis comparing the methods using a benchmark and a private dataset acquired on the field, with almost 20K of images covering a variety of scene conditions.