arXiv (All)
open access
Analytical and Convolutional Neural Network-Based Motion-Vector Propagation for Efficient Video Object Detection
Continuous video analytics requires accurate localization at low latency within embedded power budgets. This paper presents a hardware-software design methodology that reuses codec motion vectors (MVs) between detector invocations. Two alternative models support translation and scale changes: analyt
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
- CoVisco: Codec-Native Vision Encoder with Native Token Compression for Unified Image-Video Understanding#1145091
- Scalable Neural Video Representation Compression#927571
- Albireo: Adaptive, Energy-Efficient Inference Framework for Video Object Detection on the Edge#1077576
- Rethinking Object-Centric Representations for Video Dynamics Modeling#1128614
- Content-aware scalability-type selection for rate adaptation of scalable video#935038
Record · ID 1119215
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