Faster R-CNN based on frame difference and spatiotemporal context for vehicle detection

Vehicle detection is a very important part in intelligent transportation system. In order to improve the detection speed without sacrificing the accuracy, this paper propose an improved Faster R-CNN algorithm based on frame difference and spatiotemporal context to realize real-time detection of vehicles. We improve the training and testing speed of Faster R-CNN by improving the RPN module. Different from the original Faster R-CNN’s anchor based strategy, the inter frame difference in this paper is mainly used to extract the region of interest of the target. And we introduce spatiotemporal context information to assist our detection. Among them, the spatial context is formed by adding the association information outside the target area to enhance the expression of target information and improve the accuracy of target detection, while the anchor filtering of the original Faster R-CNN can be carried out by integrating the temporal context information, so as to improve the detection efficiency. This improved RPN has strong pertinence to the detection of moving vehicles. This strategy not only makes our branch network parallelly process with the original Faster R-CNN, but also avoids the extra time consumption caused by the addition of algorithm. More importantly, it can be simply added to the existing Faster R-CNN based application system without algorithm adjustment or network retraining. Experimental results show that the proposed method has high detection efficiency and low sensitivity to background changes.