Traffic Object Detection Based on Deep Learning with Region of Interest Selection

Traffic multi-target detection involves several issues, including obstruction of complex background, light variations, object occlusion, and sliding window time consumption. Hence, to improve the real-time accuracy of traffic object detection, a rapid detection algorithm incorporating region of interest based on spatio-temporal interest point (STIP) is proposed. Pixel-level STIP can provide robustness while addressing the issue of target occlusion. By employing the said feature, background suppression and spatio-temporal constraints were adopted for reducing the interference of ineffectual interest points, whose detection is based on conventional interest point detection algorithms. The mean shift clustering method was enhanced for varying the number of cluster centers in accordance with the number of objects. The candidate points of interest detected near the multi-target region were then clustered for obtaining their respective target cluster center position information. Furthermore, the region of interest was attained by combining the relative positional relation between STIP and the cluster center points. The selective search algorithm was thereby applied in the region of interest for obtaining approximately 1 000 to 2 000 candidate regions. The candidate regions were incorporated into the convolutional neural network model for feature extraction. The extracted features were input to a support vector machine for classification, and a regression model was employed for precisely correcting the position of the object recognition box. The obtained experimental results demonstrate that the number of candidate regions can be reduced by 82%, and the execution time of the algorithm can be reduced by 74%, which in turn can meet the demands of intelligent traffic monitoring. © 2018, Editorial Department of China Journal of Highway and Transport. All right reserved.