Detecting customers' buying events on a real-life database

Video Analytics covers a large set of methodologies which aim at automatically extracting information from video material. In the context of retail, the possibility to effortlessly gather statistics on customer shopping behavior is very attractive. In this work, we focus on the task of automatic classification of customer behavior, with the objecting to recognize buying events. The experiments are performed on several hours of video collected in a supermarket. Given the vast effort of the research community on the task of tracking, we assume the existence of a video tracking system capable of producing a trajectory for every individual, and currently manually annotate the input videos with trajectories. From the annotated video recordings, we extract features related to the spatio-temporal behavior of the trajectory, and to the user movement, and analyze the shopping sequences using a Hidden Markov Model (HMM). First results show that it is possible to discriminate between buying and non-buying behavior with an accuracy of 74%.