  Body-worn video (BWV) cameras are increasingly utilized by police departments
to provide a record of police-public interactions. However, large-scale BWV
deployment produces terabytes of data per week, necessitating the development
of effective computational methods to identify salient changes in video. In
work carried out at the 2016 RIPS program at IPAM, UCLA, we present a novel
two-stage framework for video change-point detection. First, we employ
state-of-the-art machine learning methods including convolutional neural
networks and support vector machines for scene classification. We then develop
and compare change-point detection algorithms utilizing mean squared-error
minimization, forecasting methods, hidden Markov models, and maximum likelihood
estimation to identify noteworthy changes. We test our framework on detection
of vehicle exits and entrances in a BWV data set provided by the Los Angeles
Police Department and achieve over 90% recall and nearly 70% precision --
demonstrating robustness to rapid scene changes, extreme luminance differences,
and frequent camera occlusions.
