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IEEE Transactions on Pattern Analysis and Machine Intelligence
Resolving Motion Correspondence for Densely Moving Points
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Contextual Priming for Object Detection
International Journal of Computer Vision
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
Contour-Based Object Tracking with Occlusion Handling in Video Acquired Using Mobile Cameras
IEEE Transactions on Pattern Analysis and Machine Intelligence
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The Function Space of an Activity
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Tracking by Affine Kernel Transformations Using Color and Boundary Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semantic Representation and Recognition of Continued and Recursive Human Activities
International Journal of Computer Vision
Unified video annotation via multigraph learning
IEEE Transactions on Circuits and Systems for Video Technology
Correlative linear neighborhood propagation for video annotation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Human activity analysis: A review
ACM Computing Surveys (CSUR)
Kernel-based object tracking using asymmetric kernels with adaptive scale and orientation selection
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Automatic traffic surveillance system for vehicle tracking and classification
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A probabilistic framework for semantic video indexing, filtering,and retrieval
IEEE Transactions on Multimedia
Video Annotation Based on Kernel Linear Neighborhood Propagation
IEEE Transactions on Multimedia
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IEEE Transactions on Circuits and Systems for Video Technology
A Survey on Visual Content-Based Video Indexing and Retrieval
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Multimodal Video Indexing and Retrieval Using Directed Information
IEEE Transactions on Multimedia
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Video event description is an important research topic in video analysis with a vast amount of applications, such as visual surveillance, video retrieval, video annotation, video database indexing, and interactive system. In this paper, we present a framework for automated video event description, which features fused with the context knowledge to provide accurate and reliable event description. The processing framework is designed to describe the event and recognize objects activities composed of four components: object detection, classification, tracking, and semantic event description. Our contribution is to integrate the contextual cues into these components to facilitate the semantic video event description. Furthermore, in the tracking part, a novel adaptive shape kernel based mean shift tracking algorithm is proposed to improve object tracking performance under object deformation and background clutter. In the experiments, we show attractive experimental results, highlighting the system efficiency and tracking capability by using our video event description system on a real-world video for video event understanding application.