Multitarget Tracking Algorithm Based on an Improved YOLOv3 Algorithm

To solve the problem of high missed rate and slow detection rate in the current multitarget tracking process, a multitarget tracking algorithm with an improved YOLOv3 network structure is proposed. First, the K means+ algorithm is utilized to cluster the target boundaries in the dataset. The priori parameters of the network are optimized using the clustering results. Then, the deep separable convolution module is employed instead of standard convolution in the Darknet-53 feature extraction layer, thereby reducing the number of parameters. In addition, the key channel information of the feature map is highlighted by applying the SENet module in the YOLO prediction layer. Finally, the improved YOLOv3 algorithm is used to implement the detection of a target in the classic tracking-by-detection framework. Meanwhile, the Deep-SORT algorithm is adopted in the tracking part. Experimental results show that the proposed multitarget tracking algorithm can effectively reduce the missed detection rate and take into account the detection accuracy and real-time performance, simultaneously.