Multi Feature Path Modeling for Video Surveillance
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Who will be the customer?: a social robot that anticipates people's behavior from their trajectories
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Beyond pixels: exploring new representations and applications for motion analysis
Beyond pixels: exploring new representations and applications for motion analysis
Human action recognition using ordinal measure of accumulated motion
EURASIP Journal on Advances in Signal Processing - Special issue on video analysis for human behavior understanding
Human behavior analysis at a point of sale
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
A Survey of Vision-Based Trajectory Learning and Analysis for Surveillance
IEEE Transactions on Circuits and Systems for Video Technology
Shopping behavior recognition using a language modeling analogy
Pattern Recognition Letters
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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%.