Real-time anomaly detection in full motion video

Glenn Konowicz; Jiang Li

doi:10.1117/12.919365

25 May 2012 Real-time anomaly detection in full motion video

Glenn Konowicz, Jiang Li

Proceedings Volume 8386, Full Motion Video (FMV) Workflows and Technologies for Intelligence, Surveillance, and Reconnaissance (ISR) and Situational Awareness; 83860I (2012) https://doi.org/10.1117/12.919365
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

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.

Citation Download Citation

Glenn Konowicz and Jiang Li "Real-time anomaly detection in full motion video", Proc. SPIE 8386, Full Motion Video (FMV) Workflows and Technologies for Intelligence, Surveillance, and Reconnaissance (ISR) and Situational Awareness, 83860I (25 May 2012); https://doi.org/10.1117/12.919365

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KEYWORDS

Detection and tracking algorithms

Video

Video surveillance

Motion detection

Video acceleration

Motion models

Roads

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