# Multi-Object Tracking with Multiple Cues and Switcher-Aware Classification

arXiv: https://arxiv.org/abs/1901.06129 · HTML: https://mlsanity.com/paper/1901.06129/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jan 2019.

## Built on by (most cited first, 16 of 16)

- [StrongSORT: Make DeepSORT Great Again](https://mlsanity.com/paper/2202.13514/) (2022)
- [MeMOT: Multi-Object Tracking with Memory](https://mlsanity.com/paper/2203.16761/) (2022)
- [SG-Net: Spatial Granularity Network for One-Stage Video Instance Segmentation](https://mlsanity.com/paper/2103.10284/) (2021)
- [Tracking Objects as Pixel-wise Distributions](https://mlsanity.com/paper/2207.05518/) (2022)
- [Deep Learning for UAV-based Object Detection and Tracking: A Survey](https://mlsanity.com/paper/2110.12638/) (2021)
- [One More Check: Making "Fake Background" Be Tracked Again](https://mlsanity.com/paper/2104.09441/) (2021)
- [Self-Supervised Multi-Object Tracking with Cross-Input Consistency](https://mlsanity.com/paper/2111.05943/) (2021)
- [City-Scale Multi-Camera Vehicle Tracking Guided by Crossroad Zones](https://mlsanity.com/paper/2105.06623/) (2021)
- [Self-Supervised Multi-Object Tracking with Path Consistency](https://mlsanity.com/paper/2404.05136/) (2024)
- [Online Multiple Object Tracking with Cross-Task Synergy](https://mlsanity.com/paper/2104.00380/) (2021)
- [Local Metrics for Multi-Object Tracking](https://mlsanity.com/paper/2104.02631/) (2021)
- [Recent Advances in Embedding Methods for Multi-Object Tracking: A Survey](https://mlsanity.com/paper/2205.10766/) (2022)
- [A Certifiable Security Patch for Object Tracking in Self-Driving Systems via Historical Deviation Modeling](https://mlsanity.com/paper/2207.08556/) (2022)
- [STURE: Spatial-Temporal Mutual Representation Learning for Robust Data Association in Online Multi-Object Tracking](https://mlsanity.com/paper/2201.06824/) (2022)
- [UnsMOT: Unified Framework for Unsupervised Multi-Object Tracking with Geometric Topology Guidance](https://mlsanity.com/paper/2309.01078/) (2023)
- [An Approximate Dynamic Programming Framework for Occlusion-Robust Multi-Object Tracking](https://mlsanity.com/paper/2405.15137/) (2024)

## Abstract (arXiv)

In this paper, we propose a unified Multi-Object Tracking (MOT) framework learning to make full use of long term and short term cues for handling complex cases in MOT scenes. Besides, for better association, we propose switcher-aware classification (SAC), which takes the potential identity-switch causer (switcher) into consideration. Specifically, the proposed framework includes a Single Object Tracking (SOT) sub-net to capture short term cues, a re-identification (ReID) sub-net to extract long term cues and a switcher-aware classifier to make matching decisions using extracted features from the main target and the switcher. Short term cues help to find false negatives, while long term cues avoid critical mistakes when occlusion happens, and the SAC learns to combine multiple cues in an effective way and improves robustness. The method is evaluated on the challenging MOT benchmarks and achieves the state-of-the-art results.