# Towards High Performance Video Object Detection for Mobiles

Source: [Towards High Performance Video Object Detection for Mobiles](<https://www.sota2.com/research/paper/68fa64491e209131cea03bf3>)

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

Data updated: 2025-10-28T13:56:35.711Z

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

Published: 2018-04-16T17:59:36.000Z

## Abstract

Despite the recent success of video object detection on Desktop GPUs, its architecture is still far too heavy for mobiles. It is also unclear whether the key principles of sparse feature propagation and multi-frame feature aggregation apply at very limited computational resources. In this paper, we present a light weight network architecture for video object detection on mobiles. Light weight image object detector is applied on sparse key frames. A very small network, Light Flow, is designed for establishing correspondence across frames. A flow-guided GRU module is designed to effectively aggregate features on key frames. For non-key frames, sparse feature propagation is performed. The whole network can be trained end-to-end. The proposed system achieves 60.2% mAP score at speed of 25.6 fps on mobiles (e.g., HuaWei Mate 8).

## 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 (%): 62.8.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1804.05830>)

[PDF](<https://arxiv.org/pdf/1804.05830.pdf>)