Vision based ADAS for Forward Vehicle Detection using Convolutional Neural Networks and Motion Tracking

With the rapid development of advanced driving assistance technologies, from the very beginning of parking assistance, lane departure warning, forward collision warning, to active distance control cruise, the active safety protection of vehicles has gained the popularity in recent years. However, there are several important issues in the image based forward collision warning systems. If the characteristics of vehicles are defined manually for detection, we need to consider various conditions to set the threshold to fit a variety of the environment change. Although the state-of-art machine learning methods can provide more accurate results then ever, the required computation cost is far much higher. In order to find a balance between these two approaches, we present a detection-tracking technique for forward collision warning. The motion tracking algorithm is built on top of the convolutional neural networks for vehicle detection. For all processed image frames, the ratio between detection and tracking is well adjusted to achieve a good performance with an accuracy/computation trade-off. Th experiments with real-time results are presented with a GPU computing platform.