[Submitted on 17 Oct 2026]
Title:CodecMotion-OAD: Efficient Online Action Detection with Compressed-Domain Motion
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Abstract: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 features with a pretrained flow-feature interface without action labels and then trains RGB+MV detectors using ground-truth labels and soft predictions from a frozen RGB+Flow teacher. To the best of our knowledge, this is the first application of codec MVs to frame-level OAD. On THUMOS'14 with MiniROAD, CodecMotion-OAD achieves 68.3 mAP versus 70.9 mAP for matched-distillation RGB+Flow, while reducing decoder-to-prediction processing time by 11.48-fold on CPU and 2.04-fold on GPU.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2610.26915 [cs.CV]
(or arXiv:2610.26915v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.26915
arXiv-issued DOI via DataCite
Submission history
From: Zepeng Sun [view email] [v1] Sat, 17 Oct 2026 06:31:21 UTC (3,621 KB)
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