Video activity detection using compressed domain motion trajectories for H.264 videos

Surveillance videos are often compressed for transmission or storage. It is desirable to be able to perform automatic event detection in the compressed domain directly. In this paper, we investigate the use of motion trajectories for video activity detection in the compressed domain. We show that it...

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Bibliographic Details
Main Authors: Haowei Liu, Ming-Ting Sun, Ruei-Cheng Wu, Shiaw-Shian Yu
Format: Conference Proceeding
Language:English
Subjects:
Data mining
Event detection
Motion detection
Motion estimation
Space technology
Streaming media
Tracking
Trajectory
Transform coding
Video compression
Online Access:Request full text
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Description
Summary:Surveillance videos are often compressed for transmission or storage. It is desirable to be able to perform automatic event detection in the compressed domain directly. In this paper, we investigate the use of motion trajectories for video activity detection in the compressed domain. We show that it is possible to extract reliable motion trajectories directly from compressed H.264 video streams. To overcome the problems caused by unreliable motion vectors, we propose to include the information from the compressed domain prediction residuals to make the tracking more robust. We also show a real world application based on the classification of the motion trajectories to detect vacant or occupied parking spaces.
ISSN:0271-4302
2158-1525
DOI:10.1109/ISCAS.2010.5537763