Residual-Network-Leveraged Vehicle-Thrown-Waste Identification in Real-Time Traffic Surveillance Videos

Abstract

We attempt to intelligently identify violations of throwing waste from vehicles (TWV) in real-time traffic surveillance videos. In addition to polluting the environment, TWV easily causes injury to sanitation workers responsible for cleaning roads by passing vehicles. However, manual inspection is still the commonest way to recognize such uncivilized behavior in videos with very high time and labor-consuming. In answer to these challenges, we design a novel 20-layer residual network (Nov-ResNet-20) for training the vehicle-thrown-waste identification model (VTWIM). Then, incorporating Nov-ResNet-20, Selective Search, and Non-Maximum Suppression (NMS), we propose the deep-residual-network-leveraged vehicle-thrown-waste identification method (DRN-VTWI). Our method first splits one video frame into several regions matching suspected objects marked with location boxes via Selective Search. Then, in terms of the VTWIM trained by Nov-ResNet-20 our method identifies the regions containing TWV. Last, our method removes the redundant location boxes for each recognized, vehicle-thrown waste and only keeps the best one. The significance of our work is four-fold: 1) Nov-ResNet-20 has a moderate depth: 6 convolutional layers, 7 residual layers, and in total 20 weight layers. Due to the joint contribution of the residual, batch normalization, dropout, and cross-entropy loss, it is eligible to identify TWV using a small quantity of manually-annotated training samples. 2) Selective Search diversely marks all possible, suspected objects in video frames, whereas NMS keeps the best location box for each recognized vehicle-thrown waste, removing all redundancies. In this way, DRN-VTWI finds potential violations of TWV as many as possible and optimally annotates vehicle-thrown wastes in frames as well. 3) Combining the power of Nov-ResNet-20, Selective Search, and NMS, DRN-VTWI well solves the challenging, intelligent identification of vehicle-thrown wastes for real-time traffic surveillance. Experimental studies conducted on real-time traffic surveillance videos demonstrate the effectiveness as well as superiority of our efforts.