Sparse Message Passing Network with Feature Integration for Online Multiple Object Tracking

• URL: http://arxiv.org/abs/2212.02992v1
• Date: Tue, 6 Dec 2022 14:10:57 GMT
• Title: Sparse Message Passing Network with Feature Integration for Online Multiple Object Tracking
• Authors: Bisheng Wang, Horst Possegger, Horst Bischof, Guo Cao
• Abstract summary: Experimental results demonstrate that a simple online MPN with these two contributions can perform better than many state-of-the-art methods. Our association method generalizes well and can also improve the results of private detection based methods.
• Score: 6.510588721127479
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Existing Multiple Object Tracking (MOT) methods design complex architectures for better tracking performance. However, without a proper organization of input information, they still fail to perform tracking robustly and suffer from frequent identity switches. In this paper, we propose two novel methods together with a simple online Message Passing Network (MPN) to address these limitations. First, we explore different integration methods for the graph node and edge embeddings and put forward a new IoU (Intersection over Union) guided function, which improves long term tracking and handles identity switches. Second, we introduce a hierarchical sampling strategy to construct sparser graphs which allows to focus the training on more difficult samples. Experimental results demonstrate that a simple online MPN with these two contributions can perform better than many state-of-the-art methods. In addition, our association method generalizes well and can also improve the results of private detection based methods.

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