Inception single shot multi-box detector with affinity propagation clustering and their application in multi-class vehicle counting

Abstract

Multi-class vehicle detection and counting in video-based traffic surveillance systems with real-time performance and acceptable precision are challenging. This paper proposes a modified single shot multi-box convolutional neural network named Inception-SSD (ISSD) for vehicle detection and a centroid matching algorithm for vehicle counting. An Inception-like block is introduced to replace the extra feature layers in the original SSD to deal with the multi-scale vehicle detection to enhance smaller vehicles’ detection. Non-Maximum Suppression (NMS) is replaced with Affinity Propagation Clustering (APC) to improve the detection of nearby occluded vehicles. For a 300 × 300 input image, on PASCAL VOC 2007 test data set, the proposed ISSD achieved 79.3 mean Average Precision (mAP) and ran on an NVIDIA RTX2080Ti; the network attains a speed of 52.3 frames per second. ISSD with APC generates 2.7% improvement in mAP over original SSD300 while almost retaining its time efficiency. By centroid matching algorithm, the vehicles are counted class-wise with a weighted F1 of 98.5%, which is quite superior to the other recent existing research works.