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Displaying 1-4 out of 4 total
Pyramid Coding for Functional Scene Element Recognition in Video Scenes
2013 IEEE International Conference on Computer Vision (ICCV)
By Eran Swears,Anthony Hoogs,Kim Boyer
Issue Date:December 2013
Recognizing functional scene elements in video scenes based on the behaviors of moving objects that interact with them is an emerging problem of interest. Existing approaches have a limited ability to characterize elements such as cross-walks, intersection...
Learning and recognizing complex multi-agent activities with applications to american football plays
Applications of Computer Vision, IEEE Workshop on
By Eran Swears,Anthony Hoogs
Issue Date:January 2012
We are interested in modeling and recognizing complex behaviors in video, where multiple agents are interacting in a time-varying manner and in a spatially-localized do-main such as American football. Our approach pushes the model complexity onto the obser...
AVSS 2011 demo session: A large-scale benchmark dataset for event recognition in surveillance video
2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS 2011)
By Sangmin Oh,Anthony Hoogs,Amitha Perera,Naresh Cuntoor,Chia-Chih Chen,Jong Taek Lee,Saurajit Mukherjee,J. K. Aggarwal,Hyungtae Lee,Larry Davis,Eran Swears,Xiaoyang Wang,Qiang Ji,Kishore Reddy,Mubarak Shah,Carl Vondrick,Hamed Pirsiavash,Deva Ramanan,Jenny Yuen,Antonio Torralba,Bi Song,Anesco Fong,Amit Roy-Chowdhury,Mita Desai
Issue Date:August 2011
Summary form only given. We present a concept for automatic construction site monitoring by taking into account 4D information (3D over time), that is acquired from highly-overlapping digital aerial images. On the one hand today's maturity of flying micro ...
Learning Motion Patterns in Surveillance Video using HMM Clustering
Motion and Video Computing, IEEE Workshop on
By Eran Swears, Anthony Hoogs, A.G. Amitha Perera
Issue Date:January 2008
We present a novel approach to learning motion behavior in video, and detecting abnormal behavior, using hierarchical clustering of Hidden Markov Models (HMMs). A continuous stream of track data is used for online and on-demand creation and training of HMM...
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