Multi-Target Tracking Based on a Combined Attention Mechanism and Occlusion Sensing in a Behavior-Analysis System

Abstract

Multi-object tracking (MOT) is a topic of great interest in the field of computer vision, which is essential in smart behavior-analysis systems for healthcare, such as human-flow monitoring, crime analysis, and behavior warnings. Most MOT methods achieve stability by combining object-detection and re-identification networks. However, MOT requires high efficiency and accuracy in complex environments with occlusions and interference. This often increases the algorithm’s complexity, affects the speed of tracking calculations, and reduces real-time performance. In this paper, we present an improved MOT method combining an attention mechanism and occlusion sensing as a solution. A convolutional block attention module (CBAM) calculates the weights of space and channel attention from the feature map. The attention weights are used to fuse the feature maps to extract adaptively robust object representations. An occlusion-sensing module detects an object’s occlusion, and the appearance characteristics of an occluded object are not updated. This can enhance the model’s ability to extract object features and improve appearance feature pollution caused by the short-term occlusion of an object. Experiments on public datasets demonstrate the competitive performance of the proposed method compared with the state-of-the-art MOT methods. The experimental results show that our method has powerful data association capability, e.g., 73.2% MOTA and 73.9% IDF1 on the MOT17 dataset.

Multi-Pedestrian Tracking Based on KC-YOLO Detection and Identity Validity Discrimination Module

“…The mechanism processes only regions pertinent to the task, preserving task-relevant regions while suppressing extraneous ones. An exemplary embodiment, the Spatial Transformation Network (STN) by Google DeepMind, learns preprocessing operations from input data that align with the specific task [13].…”

“…However, real applications mostly involve complex scenes, and the size of the pedestrian detection frame produces a very large error due to the different distances of the camera from the ground. The IOU algorithm has very little utility in this case (Figure 6c), which is why it cannot be used as a calculation standard to show the occlusion of pedestrians and small targets in real applications [13]. Therefore, we propose the identity validity discriminant coefficient k, which calculates the ratio of the extent of the occluded portion of an occluded pedestrian to its detection frame and can more accurately discriminate the degree of the pedestrian's occlusion.…”

“…The algorithm effectively leverages both pedestrian depth appearance features and motion features to establish correlations among the tracking targets, thus realizing the multi-objective target tracking of pedestrians. Zhou et al [13] proposed an improved MOT approach for occlusion scenarios, combining attention mechanisms and occlusion sensing as a solution. Jia [14] designed and developed a network for learning separate representations for processing occlusion re-identification guided by semantic preference object queries in a converter without strict character image alignment or any additional supervision.…”