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Detecting Foot-Chases for Police Body-Worn Video

Undergraduate #55
Discipline: Computer Sciences and Information Management
Subcategory: Computer Science & Information Systems

Rafael Aguayo - University of California, San Diego
Co-Author(s): Alejandro Camacho, California State University, Fullerton, CA Qi Yang, University of Southern California, Los Angeles, CA Piyali Mukherjee, Columbia University, New York, NY Hayden Schaeffer, Carnegie Mellon University, Pittsburgh, PA



Existing methods to record interactions between the public and police officers are unable to capture the entirety of police-public interactions. In order to provide a comprehensive understanding of these interactions, the Los Angeles Police Department (LAPD) intends to utilize Body-Worn Video (BWV) collected from cameras fastened to their officers. BWV provides a novel means to collect fine-grained information about police-public interactions as well as provide alternative means of evidence.
The research objective is to determine the effectiveness of machine-learning algorithms in identifying specific actions from videos, in particular foot-chases. Our proposed algorithm uses semi-supervised methods such as the detection of point-features and their classification via support-vector machines, which have been implemented using MATLAB (including the Computer Vision and Statistics and Machine Learning toolbox). The detection of point-features includes using methods such as SURF, BRISK, and optical flow, which provide local and global changes in points. Our training dataset consists of 100 training videos (20 foot-chase and 80 non-foot-chase) and a test dataset of 60 LAPD videos (4 foot-chase and 56 non-foot-chase). We achieved results of 91.6% testing accuracy. From the test dataset, we also correctly identified every foot-chase occurrence, which was one of the main goals of this project. From this study, we have concluded that machine-learning techniques are a viable option for identifying foot-chases. Through widespread use of this software, it will contribute to saving valuable resources of the LAPD as their usage of BWV expands and provide a tool for the LAPD to conduct video analytics.

Funder Acknowledgement(s): This study was supported by a grant from the National Science Foundation (NSF grant DMS-0931852).

Faculty Advisor: Hayden Schaeffer,

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This material is based upon work supported by the National Science Foundation (NSF) under Grant No. DUE-1930047. Any opinions, findings, interpretations, conclusions or recommendations expressed in this material are those of its authors and do not represent the views of the AAAS Board of Directors, the Council of AAAS, AAAS’ membership or the National Science Foundation.

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