# Robust and efficient post-processing for video object detection

arXiv: https://arxiv.org/abs/2009.11050 · HTML: https://mlsanity.com/paper/2009.11050/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2020.

## Built on by (most cited first, 16 of 16)

- [TransVOD: End-to-End Video Object Detection with Spatial-Temporal Transformers](https://mlsanity.com/paper/2201.05047/) (2022)
- [End-to-End Video Object Detection with Spatial-Temporal Transformers](https://mlsanity.com/paper/2105.10920/) (2021)
- [DAP: A Dynamic Adversarial Patch for Evading Person Detectors](https://mlsanity.com/paper/2305.11618/) (2023)
- [Delving into Multi-modal Multi-task Foundation Models for Road Scene Understanding: From Learning Paradigm Perspectives](https://mlsanity.com/paper/2402.02968/) (2024)
- [YOLOV: Making Still Image Object Detectors Great at Video Object Detection](https://mlsanity.com/paper/2208.09686/) (2022)
- [FAQ: Feature Aggregated Queries for Transformer-based Video Object Detectors](https://mlsanity.com/paper/2303.08319/) (2023)
- [Spatio-Temporal Learnable Proposals for End-to-End Video Object Detection](https://mlsanity.com/paper/2210.02368/) (2022)
- [Event Neural Networks](https://mlsanity.com/paper/2112.00891/) (2021)
- [Practical Video Object Detection via Feature Selection and Aggregation](https://mlsanity.com/paper/2407.19650/) (2024)
- [SSGA-Net: Stepwise Spatial Global-local Aggregation Networks for for Autonomous Driving](https://mlsanity.com/paper/2405.18857/) (2024)
- [An Application-Agnostic Automatic Target Recognition System Using Vision Language Models](https://mlsanity.com/paper/2411.03491/) (2024)
- [Improving Token-based Object Detection with Video](https://mlsanity.com/paper/2506.22562/) (2025)
- [Virtuoso: Video-based Intelligence for real-time tuning on SOCs](https://mlsanity.com/paper/2112.13076/) (2021)
- [Unifying Tracking and Image-Video Object Detection](https://mlsanity.com/paper/2211.11077/) (2022)
- [Deteksi Sampah di Permukaan dan Dalam Perairan pada Objek Video dengan Metode Robust and Efficient Post-Processing dan Tubelet-Level Bounding Box Linking](https://mlsanity.com/paper/2307.10039/) (2023)
- [Learn Temporal Consistency For Robust Satellite Video Detector](https://mlsanity.com/paper/2606.15112/) (2026)

## Abstract (arXiv)

Object recognition in video is an important task for plenty of applications, including autonomous driving perception, surveillance tasks, wearable devices or IoT networks. Object recognition using video data is more challenging than using still images due to blur, occlusions or rare object poses. Specific video detectors with high computational cost or standard image detectors together with a fast post-processing algorithm achieve the current state-of-the-art. This work introduces a novel post-processing pipeline that overcomes some of the limitations of previous post-processing methods by introducing a learning-based similarity evaluation between detections across frames. Our method improves the results of state-of-the-art specific video detectors, specially regarding fast moving objects, and presents low resource requirements. And applied to efficient still image detectors, such as YOLO, provides comparable results to much more computationally intensive detectors.