Detection of anomalous events in shipboard video using moving object segmentation and tracking

Ben Wenger, Shreekanth Mandayam, Patrick J. Violante, Kimberly J. Drake

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Anomalous indications in monitoring equipment onboard U.S. Navy vessels must be handled in a timely manner to prevent catastrophic system failure. The development of sensor data analysis techniques to assist a ship's crew in monitoring machinery and summon required ship-to-shore assistance is of considerable benefit to the Navy. In addition, the Navy has a large interest in the development of distance support technology in its ongoing efforts to reduce manning on ships. In this paper, we present algorithms for the detection of anomalous events that can be identified from the analysis of monochromatic stationary ship surveillance video streams. The specific anomalies that we have focused on are the presence and growth of smoke and fire events inside the frames of the video stream. The algorithm consists of the following steps. First, a foreground segmentation algorithm based on adaptive Gaussian mixture models is employed to detect the presence of motion in a scene. The algorithm is adapted to emphasize gray-level characteristics related to smoke and fire events in the frame. Next, shape discriminant features in the foreground are enhanced using morphological operations. Following this step, the anomalous indication is tracked between frames using Kalman filtering. Finally, gray level shape and motion features corresponding to the anomaly are subjected to principal component analysis and classified using a multilayer perceptron neural network. The algorithm is exercised on 68 video streams that include the presence of anomalous events (such as fire and smoke) and benign/nuisance events (such as humans walking the field of view). Initial results show that the algorithm is successful in detecting anomalies in video streams, and is suitable for application in shipboard environments. One of the principal advantages of this technique is that the method can be applied to monitor legacy shipboard systems and environments where highquality, color video may not be available.

Original languageEnglish (US)
Title of host publicationAUTOTESTCON 2010
Subtitle of host publication45 Years of Support Innovation - Moving Forward at the Speed of Light, Proceedings
Pages261-266
Number of pages6
DOIs
Publication statusPublished - Dec 6 2010
Event45 Years of Support Innovation - Moving Forward at the Speed of Light, AUTOTESTCON 2010 - Orlando, FL, United States
Duration: Sep 13 2010Sep 16 2010

Publication series

NameAUTOTESTCON (Proceedings)

Other

Other45 Years of Support Innovation - Moving Forward at the Speed of Light, AUTOTESTCON 2010
CountryUnited States
CityOrlando, FL
Period9/13/109/16/10

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All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this

Wenger, B., Mandayam, S., Violante, P. J., & Drake, K. J. (2010). Detection of anomalous events in shipboard video using moving object segmentation and tracking. In AUTOTESTCON 2010: 45 Years of Support Innovation - Moving Forward at the Speed of Light, Proceedings (pp. 261-266). [5613544] (AUTOTESTCON (Proceedings)). https://doi.org/10.1109/AUTEST.2010.5613544