Human activity detection and recognition for video surveillance

Abstract

We present a framework for detecting and recognizing human activities for outdoor video surveillance applications. Our research makes the following contributions: For activity detection and tracking, we improve robustness by providing intelligent control and fail-over mechanisms, built on top of low-level motion detection algorithms such as frame differencing and feature correlation. For activity recognition, we propose an efficient representation of human activities that enables recognition of different interaction patterns among a group of people based on simple statistics computed on the tracked trajectories, without building complicated Markov chain, hidden Markov models (HMM), or coupled hidden Markov models (CHMM). We demonstrate our techniques using real-world video data to automatically distinguish normal behaviors from suspicious ones in a parking lot setting, which can aid security surveillance.

Extracted Key Phrases

4 Figures and Tables

01020'04'05'06'07'08'09'10'11'12'13'14'15'16'17
Citations per Year

117 Citations

Semantic Scholar estimates that this publication has 117 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@inproceedings{Niu2004HumanAD, title={Human activity detection and recognition for video surveillance}, author={Wei Niu and Jiao Long and Dan Han and Yuan-Fang Wang}, booktitle={ICME}, year={2004} }