Human Silhouette Segmentation for Automatic Recognition of Armed Robbery
Video surveillance has long been used in an attempt to prevent crimes by providing a ready means of identifying the perpetrator and ensuring that he or she is held accountable. Useful as this may be, it remains a passive method of crime control. Computer recognition of activities in such situations could thrust video surveillance into an active role by allowing police to be alerted automatically, hopefully in time to prevent loss of life or property.
We propose a method by which to analyze silhouettes and recognize a classic holdup position of armed robbery. In such a situation, one actor levels his or her arm while another actor raises his or her arm(s) into the air. The core of this algorithm is skeleton analysis. We attempt recognition by first segmenting the skeleton of the silhouette into separate pieces of the body, then identifying the positions of the arms. We show that our algorithm correctly utilizes skeletons to identify parts of the human body and recognize these holdup positions. The novelty and strength of our proposed method are based in its approach of analyzing the silhouette's own medial lines.
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daVitoria Lobo, Niels J.
Bachelor of Science (B.S.)
College of Engineering and Computer Science
Dissertations, Academic -- Engineering; Engineering -- Dissertations, Academic; Computer vision; Motion perception (Vision)
Length of Campus-only Access
Honors in the Major Thesis
Dever, Jaime, "Human Silhouette Segmentation for Automatic Recognition of Armed Robbery" (2005). HIM 1990-2015. 501.