# Multiple Object Tracking with Motion and Appearance Cues

arXiv: https://arxiv.org/abs/1909.00318 · HTML: https://mlsanity.com/paper/1909.00318/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2019.

## Built on by (most cited first, 5 of 5)

- [Deep Learning for UAV-based Object Detection and Tracking: A Survey](https://mlsanity.com/paper/2110.12638/) (2021)
- [TOPIC: A Parallel Association Paradigm for Multi-Object Tracking under Complex Motions and Diverse Scenes](https://mlsanity.com/paper/2308.11157/) (2023)
- [Vehicle Detection and Tracking From Surveillance Cameras in Urban Scenes](https://mlsanity.com/paper/2109.12414/) (2021)
- [Tracklet-Switch Adversarial Attack against Pedestrian Multi-Object Tracking Trackers](https://mlsanity.com/paper/2111.08954/) (2021)
- [AR2-4FV: Anchored Referring and Re-identification for Long-Term Grounding in Fixed-View Videos](https://mlsanity.com/paper/2603.07758/) (2026)

## Abstract (arXiv)

Due to better video quality and higher frame rate, the performance of multiple object tracking issues has been greatly improved in recent years. However, in real application scenarios, camera motion and noisy per frame detection results degrade the performance of trackers significantly. High-speed and high-quality multiple object trackers are still in urgent demand. In this paper, we propose a new multiple object tracker following the popular tracking-by-detection scheme. We tackle the camera motion problem with an optical flow network and utilize an auxiliary tracker to deal with the missing detection problem. Besides, we use both the appearance and motion information to improve the matching quality. The experimental results on the VisDrone-MOT dataset show that our approach can improve the performance of multiple object tracking significantly while achieving a high efficiency.