arXiv (All)
open access
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-
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- ByteTraX: Enhancing the ByteTrack Architecture with Optimised Thresholding#1142109
- Analytical and Convolutional Neural Network-Based Motion-Vector Propagation for Efficient Video Object Detection#1119215
- OASIS: Online Adaptive Video Compression via Closed-loop Feedback Control#1109715
- Event-based Action Recognition by Pre-Training on Multi-Frame Representations#1298352
- Content-aware scalability-type selection for rate adaptation of scalable video#935038
Record · ID 1402303
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive