Multiple Object Tracking Using Re-Identification Model with Attention Module

Abstract

Multi-object tracking (MOT) has gained significant attention in computer vision due to its wide range of applications. Specifically, detection-based trackers have shown high performance in MOT, but they tend to fail in occlusive scenarios such as the moment when objects overlap or separate. In this paper, we propose a triplet-based MOT network that integrates the tracking information and the visual features of the object. Using a triplet-based image feature, the network can differentiate similar-looking objects, reducing the number of identity switches over a long period. Furthermore, an attention-based re-identification model that focuses on the appearance of objects was introduced to extract the feature vectors from the images to effectively associate the objects. The extensive experimental results demonstrated that the proposed method outperforms existing methods on the ID switch metric and improves the detection performance of the tracking system.

Keywords

• computer vision
• deep learning
• multiple object tracking
• re-identification