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PANet: An End-to-end Network Based on Relative Motion for Online Multi-object Tracking by Rui Li; Baopeng Zhang; Wei Liu; Zhu Teng; Jianping Fan is a Computer Science article available to read on EtoBox.

What is PANet: An End-to-end Network Based on Relative Motion for Online Multi-object Tracking about?

The popular tracking-by-detection paradigm of multi-object tracking (MOT) takes detections of each frame as the input and associates detections from one frame to another. Existing association methods based on the relative motion have attracted attention, because they restrain the effect of noisy detections and improve the performance of MOT. However, these methods depend only on the immediately previous frame, which may easily lead to inaccurate matches and even large accumulated errors. Furthermore, multiple objects involved in occlusions are not fully exploited in these existing methods, which leads to the aggravation of inaccurate matches. Motivated by these issues, we design the pivot to represent each object and propose a novel pivot association network (PANet) for the MOT task. Specifically, pivots are learned from spatial semantic and historical contextual clues, which alleviates the dependency on the immediately previous frame. Our online tracker PANet employs pivots and a lightweight associator to localize tracklets of objects, which can inhibit noise detections and improve the accuracy of tracklet prediction by learning the correlation responses between pivots and spatial

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Author

Rui Li; Baopeng Zhang; Wei Liu; Zhu Teng; Jianping Fan

Publisher

ACM

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)