Multicamera Video Summarization and Anomaly Detection from Activity Motifs


Camera network systems generate large volumes of potentially useful data, but extracting value from multiple, related videos can be a daunting task for a human reviewer. Multicamera video summarization seeks to make this task more tractable by generating a reduced set of output summary videos that concisely capture important portions of the input set. We present a system that approaches summarization at the level of detected activity motifs and shortens the input videos by compacting the representation of individual activities. Additionally, redundancy is removed across camera views by omitting from the summary activity occurrences that can be predicted by other occurrences. The system also detects anomalous events within a unified framework and can highlight them in the summary. Our contributions are a method for selecting useful parts of an activity to present to a viewer using activity motifs and a novel framework to score the importance of activity occurrences and allow transfer of importance between temporally related activities without solving the correspondence problem. We provide summarization results for a two camera network, an eleven camera network, and data from PETS 2001. We also include results from Amazon Mechanical Turk human experiments to evaluate how our visualization decisions affect task performance.
Carter de Leo and B.S. Manjunath,
“Multicamera Video Summarization and Anomaly Detection from Activity Motifs”,
ACM Transactions on Sensor Networks, vol. 10, no. 2, May. 2014.
Node ID: 593 , DB ID: 403 , Lab: VRL , Target: Journal
Subject: [Managing Multimedia Databases] « Look up more