Research On Video Pedestrian Multi-Target Tracking Algorithm Based On Deep Learning

In recent years,multi-target tracking has gained increasing attention in various fields,including intelligent security,human-computer interaction,and video analysis due to the advancement of computer data processing capabilities.The primary goal of this task is to detect and locate targets in video sequences and associate targets between frames.Deep learning-based approaches for multi-target tracking have been developed,such as the joint detection and tracking approach.This method combines pedestrian detection and tracking tasks into a single neural network,the advantage is faster tracking speed,but the disadvantage is that when encountering situations such as blocked targets or excessively dense targets,the tracking accuracy can be significantly reduced.（1）To address the effects of inaccurate estimation of pedestrian detection bounding boxes and interference problems such as occlusion in multi-target tracking tasks,thesis proposes a method for tracking multiple pedestrians using a combination of target detection and motion estimation.Firstly,a pedestrian detection model with 34 layer residual network as the backbone,integrated multi-layer feature aggregation module and multi-task branching is designed to detect and locate each pedestrian in a video frame.Then a modified Kalman filter is used to build a pedestrian motion estimation model to predict the pedestrian position in the next video frame.Finally,based on the Hungarian algorithm,a detection-trajectory matching algorithm based on multi-stage data association is established to complete the association tracking of the same identity pedestrian in the before and after video frames.The MOTA of this pedestrian multi-target tracking method on the MOT16 and MOT17 test sets reached 75.6%and 74.3%,respectively,while the IDF₁ reached 73.1%and 72.4%.（2）Based on the pedestrian multi-target tracking method designed in（1）,thesis designs a pedestrian multi-target tracking method that combines recognition feature assistance and multi-stage data association to improve the ability to correlate pedestrian identities.The method introduces a separately trained pedestrian reidentification model to extract the reidentification features of the pedestrian and adds the features to the multi-stage data association algorithm to improve the accuracy of multi-target tracking.The re-recognition model uses ResNeSt50 as the backbone network,performs dimensionality reduction of the features through separate convolution structures and is trained on the MOT17 multi-target tracking dataset,independently.The multi-target tracking method assisted by the addition of re recognition features exhibits stronger pedestrian identity association capabilities on both MOT16 and MOT17 test sets,with IDF₁increasing by 1.9%and 2.1%compared to the method in（1）,respectively.In summary,thesis designs a detection-by-tracking multi-target tracking algorithm,which achieves excellent tracking accuracy while ensuring tracking speed by improving the target detection model,motion estimation model and data association method.The addition of a recognition model further improves the pedestrian identity association ability.Thesis has demonstrated the effectiveness of each module through a series of ablation experiments,and online testing on MOT Challenge has shown that the method designed in thesis is superior to some mainstream pedestrian multi-target tracking methods.