Hierarchical-Matching-Based Online and Real-Time Multi-Object Tracking with Deep Appearance Features

Abstract

Based on tracking-by-detection, we propose a hierarchical-matching-based online and real-time multi-object tracking approach with deep appearance features, which can effectively reduce the false positives (FP) in tracking. For the purpose of increasing the accuracy rate of data association, we define the trajectory confidence using its position information, appearance information, and the information of historical relevant detections, after which we can classify the trajectories into different levels. In order to obtain discriminative appearance features, we developed a deep convolutional neural network to extract the appearance features of objects and trained it on a large-scale pedestrian re-identification dataset. Last but not least, we used the proposed diverse and hierarchical matching strategy to associate detection and trajectory sets. Experimental results on the MOT benchmark dataset show that our proposed approach performs well against other online methods, especially for the metrics of FP and frames per second (FPS).

Pedestrian Detection Based on Feature Enhancement in Complex Scenes

“…Target detection mainly accomplishes the function of classifying and localizing objects in scenes. Target detection is the basis of target tracking [1] and intelligent vehicle obstacle avoidance [2]. In addition, target detection has been widely used in areas such as military defense and security surveillance [3][4][5].…”