Multiobject Behavior Recognition by Event Driven Selective Attention Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Recognizing Action at a Distance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Recognition of two-person interactions using a hierarchical Bayesian network
IWVS '03 First ACM SIGMM international workshop on Video surveillance
Efficient Visual Event Detection Using Volumetric Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Matching Shape Sequences in Video with Applications in Human Movement Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition of Composite Human Activities through Context-Free Grammar Based Representation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Human Pose Tracking in Monocular Sequence Using Multilevel Structured Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
A nonconservative flow field for robust variational image segmentation
IEEE Transactions on Image Processing
A streakline representation of flow in crowded scenes
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
An overview of contest on semantic description of human activities (SDHA) 2010
ICPR'10 Proceedings of the 20th International conference on Recognizing patterns in signals, speech, images, and videos
Local descriptors for spatio-temporal recognition
SCVMA'04 Proceedings of the First international conference on Spatial Coherence for Visual Motion Analysis
Machine Recognition of Human Activities: A Survey
IEEE Transactions on Circuits and Systems for Video Technology
A "string of feature graphs" model for recognition of complex activities in natural videos
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Learning spatiotemporal graphs of human activities
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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This paper proposes an end-to-end system to recognize multi-person behaviors in video, unifying different tasks like segmentation, modeling and recognition within a single optical flow based motion analysis framework. We show how optical flow can be used for analyzing activities of individual actors, as opposed to dense crowds, which is what the existing literature has concentrated on mostly. The algorithm consists of two steps - identification of motion patterns and modeling of motion patterns. Activities are analyzed using the underlying motion patterns which are formed by the optical flow field over a period of time. Streaklines are used to capture these motion patterns via integration of the flow field. To recognize the regions of interest, we utilize the Helmholtz decomposition to compute the divergence potential. The extrema or critical points of this potential indicates regions of high activity in the video, which are then represented as motion patterns by clustering the streaklines. We then present a method to compare two videos by measuring the similarity between their motion patterns using a combination of shape theory and subspace analysis. Such an analysis allows us to represent, compare and recognize a wide range of activities. We perform experiments on state-of-the-art datasets and show that the proposed method is suitable for natural videos in the presence of noise, background clutter and high intra class variations. Our method has two significant advantages over recent related approaches - it provides a single framework that takes care of both low-level and high-level visual analysis tasks, and is computationally efficient.