# Towards High Performance Video Object Detection

Source: [Towards High Performance Video Object Detection](<https://www.sota2.com/research/paper/68fa63d81e209131cea01391>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:51:10.308Z

Authors: Xizhou Zhu, Jifeng Dai, Lu Yuan, Yichen Wei

Published: 2017-11-30T18:48:45.000Z

## Abstract

There has been significant progresses for image object detection in recent years. Nevertheless, video object detection has received little attention, although it is more challenging and more important in practical scenarios.   Built upon the recent works, this work proposes a unified approach based on the principle of multi-frame end-to-end learning of features and cross-frame motion. Our approach extends prior works with three new techniques and steadily pushes forward the performance envelope (speed-accuracy tradeoff), towards high performance video object detection.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Video Object Detection on ImageNet VID (val)](<https://www.sota2.com/research/sota/video-object-detection-on-imagenet-vid-val.md>): Video Object Detection; ImageNet VID (val); mAP (%): 78.6.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1711.11577>)

[PDF](<https://arxiv.org/pdf/1711.11577.pdf>)