CodecMotion-OAD: Efficient Online Action Detection with Compressed-Domain Motion
Online action detection (OAD) identifies ongoing actions in streaming videos. Many existing OAD systems combine RGB appearance with optical flow, but dense-flow extraction remains a major computational bottleneck. Codec motion vectors (MVs) are available during decoding, avoiding a separate optical-flow computation. On THUMOS'14, optical flow exceeds MVs by 11.2 mAP points with motion-only inputs, whereas the gap narrows to 3.7 points after RGB fusion, with the largest reduction in action-category discrimination. We propose CodecMotion-OAD, which replaces optical flow with decoder-provided MVs while retaining existing temporal detector architectures. Its two-stage training first aligns MV...
| Authors | Zepeng Sun, Naichuan Zheng, Hailun Xia et al. |
|---|---|
| Primary category | cs.CV |
| Published | 17 October 2026 |
| Last updated at source | 17 October 2026 |
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