# Eliminating Exposure Bias and Loss-Evaluation Mismatch in Multiple Object Tracking

arXiv: https://arxiv.org/abs/1811.10984 · HTML: https://mlsanity.com/paper/1811.10984/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2018.

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

- [Deep Learning Serves Traffic Safety Analysis: A Forward-looking Review](https://mlsanity.com/paper/2203.10939/) (2022)
- [Multi-view Tracking Using Weakly Supervised Human Motion Prediction](https://mlsanity.com/paper/2210.10771/) (2022)
- [Improving Low-Resource Machine Translation via Round-Trip Reinforcement Learning](https://mlsanity.com/paper/2601.12535/) (2026)
- [Multi-target tracking for video surveillance using deep affinity network: a brief review](https://mlsanity.com/paper/2110.15674/) (2021)
- [MD-GAN with multi-particle input: the machine learning of long-time molecular behavior from short-time MD data](https://mlsanity.com/paper/2202.00995/) (2022)

## Abstract (arXiv)

Identity Switching remains one of the main difficulties Multiple Object Tracking (MOT) algorithms have to deal with. Many state-of-the-art approaches now use sequence models to solve this problem but their training can be affected by biases that decrease their efficiency. In this paper, we introduce a new training procedure that confronts the algorithm to its own mistakes while explicitly attempting to minimize the number of switches, which results in better training. We propose an iterative scheme of building a rich training set and using it to learn a scoring function that is an explicit proxy for the target tracking metric. Whether using only simple geometric features or more sophisticated ones that also take appearance into account, our approach outperforms the state-of-the-art on several MOT benchmarks.