MVTrack: Ultrafast Appearance-Free Moving Object Tracking from Compressed Bitstreams

2026-08-11

AI Summary

This work addresses the high computational cost of existing RGB-based video object tracking methods, which hinders their large-scale deployment. The authors propose MVTrack, the first approach to achieve efficient tracking solely using motion vectors extracted from H.264 compressed bitstreams, entirely bypassing pixel-domain processing. MVTrack integrates a lightweight motion vector field detector (MVDet) with a minimalistic motion association module (MVLink) to enable accurate tracking without video decoding. Evaluated on the VIRAT dataset, MVTrack outperforms YOLOv2tiny in tracking accuracy while using 60× fewer parameters, requiring 40× lower FLOPs, and achieving 8.6× faster CPU inference speed, thereby significantly advancing the practicality and efficiency of compressed-domain tracking.

Abstract

Deploying modern video trackers at scale is bottlenecked by the computational cost of RGB-based object detectors. To this end, we present MVTrack, an ultrafast tracker for moving objects that operates directly on H.264 bitstreams. MVTrack combines MVDet, a lightweight detector for motion vector fields, with MVLink, a minimalist kinematic association module. On VIRAT, MVTrack outperforms YOLO26n while using 60$\times$ fewer parameters, requiring 40$\times$ fewer FLOPs, and reducing CPU latency by 8.6$\times$. These results demonstrate that compressed video data alone can enable accurate and scalable surveillance tracking, thereby bypassing the need for pixel reconstruction.

Problem

• video tracking
• compressed bitstreams
• computational cost
• moving object detection
• scalable surveillance

Innovation

• compressed-domain tracking
• motion vector
• appearance-free
• ultrafast object tracking
• bitstream-based detection