Detecting Stochastically Scheduled Activities in Video

Octavian Udrea, Massimiliano Albanese, Vincezo Moscato, Antonio Picariello, V.S. Subrahmanian

The ability to automatically detect activities in video is of increasing importance in applications such as bank security, airport tarmac security, baggage area security and building site surveillance. We present a stochastic activity model composed of atomic actions which are directly observable through image understanding primitives. We focus on answering two types of questions: (i) what are the minimal sub-videos in which a given action is identified with probability above a certain threshold and (ii) for a given video, can we decide which activity from a given set most likely occurred? We provide the MPS algorithm for the first problem, as well as two different algorithms (naiveMPA and MPA) to solve the second. Our experimental results on a dataset consisting of staged bank robbery videos show that our algorithms are both fast and provide high quality results when compared to human reviewers.