Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition of Visual Activities and Interactions by Stochastic Parsing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Layered Representations for Human Activity Recognition
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Karma: knowledge-based active representations for metaphor and aspect
Karma: knowledge-based active representations for metaphor and aspect
Ontology and Taxonomy Collaborated Framework for Meeting Classification
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Towards automatic analysis of social interaction patterns in a nursing home environment from video
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
An Ontology for Video Event Representation
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 7 - Volume 07
Automatic video interpretation: a novel algorithm for temporal scenario recognition
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Detecting unusual activity in video
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Key frame-based activity representation using antieigenvalues
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Tracking video objects in cluttered background
IEEE Transactions on Circuits and Systems for Video Technology
Detecting abnormal activities in video sequences
Proceedings of the 2008 Ambi-Sys workshop on Ambient media delivery and interactive television
Event Model Learning from Complex Videos using ILP
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Finding "unexplained" activities in video
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
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The ability to automatically detect activities in video is of increasing importance in applications such as bank security, airport tarmac security, baggage area security and building site surveillance. We present a stochastic activity model composed of atomic actions which are directly observable through image understanding primitives. We focus on answering two types of questions: (i) what are the minimal sub-videos in which a given action is identified with probability above a certain threshold and (ii) for a given video, can we decide which activity from a given set most likely occurred? We provide the MPS algorithm for the first problem, as well as two different algorithms (naive MPA and MPA) to solve the second. Our experimental results on a dataset consisting of staged bank robbery videos (described in [Vu et al., 2003]) show that our algorithms are both fast and provide high quality results when compared to human reviewers.