# Similarity Mapping with Enhanced Siamese Network for Multi-Object Tracking

arXiv: https://arxiv.org/abs/1609.09156 · HTML: https://mlsanity.com/paper/1609.09156/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2016.

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

- [Deep Learning Serves Traffic Safety Analysis: A Forward-looking Review](https://mlsanity.com/paper/2203.10939/) (2022)
- [Simple online and real-time tracking with occlusion handling](https://mlsanity.com/paper/2103.04147/) (2021)
- [Warm-starting DARTS using meta-learning](https://mlsanity.com/paper/2205.06355/) (2022)
- [How good are deep learning methods for automated road safety analysis using video data? An experimental study](https://mlsanity.com/paper/2503.09807/) (2025)

## Abstract (arXiv)

Multi-object tracking has recently become an important area of computer vision, especially for Advanced Driver Assistance Systems (ADAS). Despite growing attention, achieving high performance tracking is still challenging, with state-of-the- art systems resulting in high complexity with a large number of hyper parameters. In this paper, we focus on reducing overall system complexity and the number hyper parameters that need to be tuned to a specific environment. We introduce a novel tracking system based on similarity mapping by Enhanced Siamese Neural Network (ESNN), which accounts for both appearance and geometric information, and is trainable end-to-end. Our system achieves competitive performance in both speed and accuracy on MOT16 challenge, compared to known state-of-the-art methods.