Multi-Target Tracking of Person Based on Deep Learning

Abstract

To improve the tracking accuracy of persons in the surveillance video, we proposed an algorithm for multi-target tracking persons based on deep learning. In this paper, we used You Only Look Once v5 (YOLOv5) to obtain person targets of each frame in the video and used Simple Online and Realtime Tracking with a Deep Association Metric (DeepSORT) to do cascade matching and Intersection Over Union (IOU) matching of person targets between different frames. To solve the IDSwitch problem caused by the low feature extraction ability of the Re-Identification (ReID) network in the process of cascade matching, we introduced Spatial Relation-aware Global Attention (RGA-S) and Channel Relation-aware Global Attention (RGA-C) attention mechanisms into the network structure. The pre-training weights are loaded for Transfer Learning training on the dataset CUHK03. To enhance the discrimination performance of the network, we proposed a new loss function design method, which introduces the Hard-Negative-Mining way into the benchmark triplet loss.To improve the classification accuracy of the network, we introduced a Label-Smoothing regularization method to the cross-entropy loss. To facilitate the model's convergence stability and convergence speed at the early training stage and to prevent the model from oscillating around the global optimum due to excessive learning rate at the later stage of training, this paper proposed a learning rate regulation method combining Linear-Warmup and exponential decay. The experimental results on CUHK03 show that the mean Average Precision (mAP) of the improved ReID network is 76.5%. The Top 1 is 42.5%, the Top 5 is 65.4%, and the Top 10 is 74.3% in Cumulative Matching Characteristics (CMC); Compared with the original algorithm, the tracking accuracy of the optimized DeepSORT tracking algorithm is improved by 2.5%, the tracking precision is improved by 3.8%. The number of identity switching is reduced by 25%. The algorithm effectively alleviates the IDSwitch problem, improves the tracking accuracy of persons, and has a high practical value.

Vehicle Multi-Object Detection and Tracking Algorithm Based on Improved You Only Look Once 5s Version and DeepSORT

“…Object tracking has important application value in the field of computer vision and artificial intelligence [4][5][6][7], and DeepSORT, as a deep-learning-based object tracking algorithm, has attracted widespread attention from scholars worldwide [8][9][10][11][12][13][14][15]. In recent In the research field based on DeepSORT, Li et al introduced the channel-relationaware global attention (RGA-C) and spatial relation-aware global attention (RGA-S) mechanisms into the network structure, and the Hard-Negative Mining Way was introduced into the basic triple loss to improve the accuracy of DeepSORT [7]. Wang et al combined gray and RGB features using Iterative Deep Aggregation (IDA) to reduce the error rate of the model [10].…”

Multi-Object Tracking Model Based on Detection Tracking Paradigm in Panoramic Scenes

“…In 2017, Li et al introduced the DeepSORT [3] tracking algorithm, which builds on the SORT algorithm by incorporating deep learning feature representations to enhance tracking performance. It primarily addresses identity switches through appearance information and introduces a more complex motion model to handle occlusions.…”