# Multiple object tracking with context awareness

arXiv: https://arxiv.org/abs/1411.7935 · HTML: https://mlsanity.com/paper/1411.7935/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2014.

## Built on by (most cited first, 4 of 4)

- [Edge Video Analytics: A Survey on Applications, Systems and Enabling Techniques](https://mlsanity.com/paper/2211.15751/) (2022)
- [Spatialyze: A Geospatial Video Analytics System with Spatial-Aware Optimizations](https://mlsanity.com/paper/2308.03276/) (2023)
- [A Hybrid Approach To Real-Time Multi-Object Tracking](https://mlsanity.com/paper/2308.01248/) (2023)
- [Comparative study of multi-person tracking methods](https://mlsanity.com/paper/2310.04825/) (2023)

## Abstract (arXiv)

Multiple people tracking is a key problem for many applications such as surveillance, animation or car navigation, and a key input for tasks such as activity recognition. In crowded environments occlusions and false detections are common, and although there have been substantial advances in recent years, tracking is still a challenging task. Tracking is typically divided into two steps: detection, i.e., locating the pedestrians in the image, and data association, i.e., linking detections across frames to form complete trajectories. For the data association task, approaches typically aim at developing new, more complex formulations, which in turn put the focus on the optimization techniques required to solve them. However, they still utilize very basic information such as distance between detections. In this thesis, I focus on the data association task and argue that there is contextual information that has not been fully exploited yet in the tracking community, mainly social context and spatial context coming from different views.