The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Action at a Distance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Efficient adaptive density estimation per image pixel for the task of background subtraction
Pattern Recognition Letters
ACM Computing Surveys (CSUR)
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Free viewpoint action recognition using motion history volumes
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Human Activity Recognition with Metric Learning
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
A survey on vision-based human action recognition
Image and Vision Computing
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
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Machine Recognition of Human Activities: A Survey
IEEE Transactions on Circuits and Systems for Video Technology
Detecting customers' buying events on a real-life database
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Pattern Recognition Letters
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This paper presents a method that analyzes human behavior in a shopping setting. Several actions are detected and we are especially interested in detecting interactions between customers and products. This paper first presents our application context, the advantages and constraint of a shopping setting. Then we present and evaluate several methods for human behavior understanding. Human actions are represented with Motion History Image (MHI), Accumulated Motion Image (AMI), Local Motion Context (LMC), and Interaction Context (IC). Then we use Support Vector Machines (SVM) to classify actions. Finally, we combine LMC and IC descriptors in a real-time system that recognizes human behaviors while shopping to enhance digital media impact at the point of sale.