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Displaying 1-4 out of 4 total
Pyramid Coding for Functional Scene Element Recognition in Video Scenes
Found in: 2013 IEEE International Conference on Computer Vision (ICCV)
By Eran Swears,Anthony Hoogs,Kim Boyer
Issue Date:December 2013
pp. 345-352
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
Found in: Applications of Computer Vision, IEEE Workshop on
By Eran Swears,Anthony Hoogs
Issue Date:January 2012
pp. 409-416
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
Found in: 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
pp. 527-528
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
Found in: Motion and Video Computing, IEEE Workshop on
By Eran Swears, Anthony Hoogs, A.G. Amitha Perera
Issue Date:January 2008
pp. 1-8
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...