
Proceedings Paper
Real-time anomaly detection in full motion videoFormat | Member Price | Non-Member Price |
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Paper Abstract
Improvement in sensor technology such as charge-coupled devices (CCD) as well as constant incremental improvements
in storage space has enabled the recording and storage of video more prevalent and lower cost than ever before.
However, the improvements in the ability to capture and store a wide array of video have required additional manpower
to translate these raw data sources into useful information. We propose an algorithm for automatically detecting
anomalous movement patterns within full motion video thus reducing the amount of human intervention required to
make use of these new data sources. The proposed algorithm tracks all of the objects within a video sequence and
attempts to cluster each object's trajectory into a database of existing trajectories. Objects are tracked by first
differentiating them from a Gaussian background model and then tracked over subsequent frames based on a
combination of size and color. Once an object is tracked over several frames, its trajectory is calculated and compared
with other trajectories earlier in the video sequence. Anomalous trajectories are differentiated by their failure to cluster
with other well-known movement patterns. Adding the proposed algorithm to an existing surveillance system could
increase the likelihood of identifying an anomaly and allow for more efficient collection of intelligence data.
Additionally, by operating in real-time, our algorithm allows for the reallocation of sensing equipment to those areas
most likely to contain movement that is valuable for situational awareness.
Paper Details
Date Published: 25 May 2012
PDF: 9 pages
Proc. SPIE 8386, Full Motion Video (FMV) Workflows and Technologies for Intelligence, Surveillance, and Reconnaissance (ISR) and Situational Awareness, 83860I (25 May 2012); doi: 10.1117/12.919365
Published in SPIE Proceedings Vol. 8386:
Full Motion Video (FMV) Workflows and Technologies for Intelligence, Surveillance, and Reconnaissance (ISR) and Situational Awareness
Donnie Self, Editor(s)
PDF: 9 pages
Proc. SPIE 8386, Full Motion Video (FMV) Workflows and Technologies for Intelligence, Surveillance, and Reconnaissance (ISR) and Situational Awareness, 83860I (25 May 2012); doi: 10.1117/12.919365
Show Author Affiliations
Glenn Konowicz, Old Dominion Univ. (United States)
Jiang Li, Old Dominion Univ. (United States)
Published in SPIE Proceedings Vol. 8386:
Full Motion Video (FMV) Workflows and Technologies for Intelligence, Surveillance, and Reconnaissance (ISR) and Situational Awareness
Donnie Self, Editor(s)
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