Summary of Samba: Synchronized Set-of-sequences Modeling For Multiple Object Tracking, by Mattia Segu et al.
Samba: Synchronized Set-of-Sequences Modeling for Multiple Object Tracking
by Mattia Segu, Luigi Piccinelli, Siyuan Li, Yung-Hsu Yang, Bernt Schiele, Luc Van Gool
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers address the challenge of tracking multiple objects in complex scenarios like dance performances or team sports, where objects move in coordinated patterns, occlude each other, and exhibit long-term dependencies. They introduce Samba, a novel model that jointly processes multiple tracklets by synchronizing their state spaces and predicting future trajectories while maintaining synchronized long-term memory representations. This approach is integrated into the tracking-by-propagation framework to create SambaMOTR, which effectively addresses long-range dependencies, interdependencies among tracklets, and temporal occlusions. The authors also propose MaskObs for dealing with uncertain observations and an efficient training recipe to scale SambaMOTR to longer sequences. Experimental results on DanceTrack, BFT, and SportsMOT datasets demonstrate significant improvements over state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Samba is a new way to track many objects moving together in complex situations. Imagine trying to follow multiple people doing a dance routine or playing sports. The objects move in patterns, hide each other, and have long-term memories of where they’ve been. This makes it hard for computers to keep track of them. Samba is designed to solve this problem by linking the movements of all the objects together. It predicts what will happen next based on what happened before, while keeping an eye on everything happening simultaneously. This helps SambaMOTR, a tracking system that uses Samba, do a great job at following the objects even when they hide each other. |
Keywords
» Artificial intelligence » Tracking
