arXiv (All)
open access
Smart Surveillance: Deep Learning and Hybrid Approaches for Dynamic Scene Analysis, Object Detection and Motion Tracking
Event-triggered recording reduces the footage a surveillance camera must store, but the trigger decides how much idle video is kept, how many events are missed, and how much computation is spent. We compare six motion triggers (frame differencing, dense optical flow, MOG2, KNN, CNT, GSOC), a CNN det
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
- Fast Feature Field ($\text{F}^3$): A Predictive Representation of Events#1355936
- Official Review · ICLR 2025#787230
- Person-of-interest centric timelapse video with AI input on home security … — Ambarella International Lp (US11869241B2)#607538
- Rate as a Feature, Not a Nuisance: Curriculum FiLM Training for Event-Based Graph Neural Networks#1402321
- Multi-Scale Latent Temporal Compression Attention and Video-Text Cross Attention Fusion for Video Action Detection#1413417
Record · ID 1466986
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