# On The Stability of Video Detection and Tracking

arXiv: https://arxiv.org/abs/1611.06467 · HTML: https://mlsanity.com/paper/1611.06467/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2016.

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

- [Multiple Object Tracking with Correlation Learning](https://mlsanity.com/paper/2104.03541/) (2021)
- [Towards Stable 3D Object Detection](https://mlsanity.com/paper/2407.04305/) (2024)
- [Improving Robustness for Pose Estimation via Stable Heatmap Regression](https://mlsanity.com/paper/2105.03569/) (2021)
- [Perception Characteristics Distance: Measuring Stability and Robustness of Perception System in Dynamic Conditions under a Certain Decision Rule](https://mlsanity.com/paper/2506.09217/) (2025)

## Abstract (arXiv)

In this paper, we study an important yet less explored aspect in video detection and tracking -- stability. Surprisingly, there is no prior work that tried to study it. As a result, we start our work by proposing a novel evaluation metric for video detection which considers both stability and accuracy. For accuracy, we extend the existing accuracy metric mean Average Precision (mAP). For stability, we decompose it into three terms: fragment error, center position error, scale and ratio error. Each error represents one aspect of stability. Furthermore, we demonstrate that the stability metric has low correlation with accuracy metric. Thus, it indeed captures a different perspective of quality. Lastly, based on this metric, we evaluate several existing methods for video detection and show how they affect accuracy and stability. We believe our work can provide guidance and solid baselines for future researches in the related areas.