Detecting shopper groups in video sequences

Abstract

We present a generalized extensible framework for automated recognition of swarming activities in video sequences. The trajectory of each individual is produced by the visual tracking sub-system and is further analyzed to detect certain types of high-level grouping behavior. We utilize recent findings in swarming behavior analysis to formulate a problem in terms of the specific distance function that we subsequently apply as part of the two-stage agglomerative clustering method to create a set of swarming events followed by a set of swarming activities. In this paper we present results for one particular type of swarming: shopper grouping. As part of this work the events detected in a relatively short time interval are further integrated into activities, the manifestation of prolonged high-level swarming behavior. The results demonstrate the ability of our method to detect such activities in congested surveillance videos. In particular in three hours of indoor retail store video, our method has correctly identified over85% of valid '"shopper-groups'" with a very low level of false positives, validated against human coded ground truth.