# Object Detection in Video with Spatial-temporal Context Aggregation

arXiv: https://arxiv.org/abs/1907.04988 · HTML: https://mlsanity.com/paper/1907.04988/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2019.

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

- [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)
- [MAMBA: Multi-level Aggregation via Memory Bank for Video Object Detection](https://mlsanity.com/paper/2401.09923/) (2024)
- [Flying Bird Object Detection Algorithm in Surveillance Video Based on Motion Information](https://mlsanity.com/paper/2301.01917/) (2023)
- [Explore Spatio-temporal Aggregation for Insubstantial Object Detection: Benchmark Dataset and Baseline](https://mlsanity.com/paper/2206.11459/) (2022)

## Abstract (arXiv)

Recent cutting-edge feature aggregation paradigms for video object detection rely on inferring feature correspondence. The feature correspondence estimation problem is fundamentally difficult due to poor image quality, motion blur, etc, and the results of feature correspondence estimation are unstable. To avoid the problem, we propose a simple but effective feature aggregation framework which operates on the object proposal-level. It learns to enhance each proposal's feature via modeling semantic and spatio-temporal relationships among object proposals from both within a frame and across adjacent frames. Experiments are carried out on the ImageNet VID dataset. Without any bells and whistles, our method obtains 80.3% mAP on the ImageNet VID dataset, which is superior over the previous state-of-the-arts. The proposed feature aggregation mechanism improves the single frame Faster RCNN baseline by 5.8% mAP. Besides, under the setting of no temporal post-processing, our method outperforms the previous state-of-the-art by 1.4% mAP.