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Displaying 1-4 out of 4 total
Similarity invariant classification of events by KL divergence minimization
Computer Vision, IEEE International Conference on
By Salman Khokhar,Imran Saleemi,Mubarak Shah
Issue Date:November 2011
This paper proposes a novel method for recognition and classification of events represented by Mixture distributions of location and flow. The main idea is to classify observed events into semantically meaningful groups even when motion is observed from di...
Scene understanding by statistical modeling of motion patterns
Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Imran Saleemi, Lance Hartung, Mubarak Shah
Issue Date:June 2010
We present a novel method for the discovery and statistical representation of motion patterns in a scene observed by a static camera. Related methods involving learning of patterns of activity rely on trajectories obtained from object detection and trackin...
Probabilistic Modeling of Scene Dynamics for Applications in Visual Surveillance
IEEE Transactions on Pattern Analysis and Machine Intelligence
By Imran Saleemi, Khurram Shafique, Mubarak Shah
Issue Date:August 2009
We propose a novel method to model and learn the scene activity, observed by a static camera. The proposed model is very general and can be applied for solution of a variety of problems. The motion patterns of objects in the scene are modeled in the form o...
Multi-source Multi-scale Counting in Extremely Dense Crowd Images
2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
By Haroon Idrees,Imran Saleemi,Cody Seibert,Mubarak Shah
Issue Date:June 2013
We propose to leverage multiple sources of information to compute an estimate of the number of individuals present in an extremely dense crowd visible in a single image. Due to problems including perspective, occlusion, clutter, and few pixels per person, ...
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