# Robust Detection of Objects under Periodic Motion with Gaussian Process Filtering

arXiv: https://arxiv.org/abs/2009.14178 · HTML: https://mlsanity.com/paper/2009.14178/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2020.

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

- [Out-Of-Distribution Detection Is Not All You Need](https://mlsanity.com/paper/2211.16158/) (2022)
- [Safety Monitoring of Machine Learning Perception Functions: a Survey](https://mlsanity.com/paper/2412.06869/) (2024)
- [TrADe Re-ID -- Live Person Re-Identification using Tracking and Anomaly Detection](https://mlsanity.com/paper/2209.06452/) (2022)
- [Benchmarking person re-identification datasets and approaches for practical real-world implementations](https://mlsanity.com/paper/2212.09981/) (2022)

## Abstract (arXiv)

Object Detection (OD) is an important task in Computer Vision with many practical applications. For some use cases, OD must be done on videos, where the object of interest has a periodic motion. In this paper, we formalize the problem of periodic OD, which consists in improving the performance of an OD model in the specific case where the object of interest is repeating similar spatio-temporal trajectories with respect to the video frames. The proposed approach is based on training a Gaussian Process to model the periodic motion, and use it to filter out the erroneous predictions of the OD model. By simulating various OD models and periodic trajectories, we demonstrate that this filtering approach, which is entirely data-driven, improves the detection performance by a large margin.