Split and Connect: A Universal Tracklet Booster for Multi-Object Tracking
Gaoang Wang, Yizhou Wang, Renshu Gu, Weijie Hu, Jenq-Neng Hwang

TL;DR
This paper introduces a universal tracklet booster for multi-object tracking that improves existing trackers by splitting and connecting tracklets, addressing challenges like occlusion and lighting variations, and demonstrates significant performance gains on benchmark datasets.
Contribution
A novel tracklet booster algorithm with a Splitter and Connector, applicable to any tracker, using temporal dilated convolutions and self-attention for improved multi-object tracking.
Findings
Significant improvements in IDF1 scores on MOT17 and MOT20 datasets.
Effective handling of occlusion and ID-switch issues.
Compatible with various existing tracking algorithms.
Abstract
Multi-object tracking (MOT) is an essential task in the computer vision field. With the fast development of deep learning technology in recent years, MOT has achieved great improvement. However, some challenges still remain, such as sensitiveness to occlusion, instability under different lighting conditions, non-robustness to deformable objects, etc. To address such common challenges in most of the existing trackers, in this paper, a tracklet booster algorithm is proposed, which can be built upon any other tracker. The motivation is simple and straightforward: split tracklets on potential ID-switch positions and then connect multiple tracklets into one if they are from the same object. In other words, the tracklet booster consists of two parts, i.e., Splitter and Connector. First, an architecture with stacked temporal dilated convolution blocks is employed for the splitting position…
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Taxonomy
TopicsVideo Surveillance and Tracking Methods · Impact of Light on Environment and Health · Advanced Neural Network Applications
MethodsDilated Convolution · Label Smoothing · Convolution
