By Yun Fu
This publication offers a special view of human job reputation, specifically fine-grained human job constitution studying, human-interaction reputation, RGB-D info dependent motion attractiveness, temporal decomposition, and causality studying in unconstrained human job movies. The concepts mentioned provide readers instruments that offer an important development over current methodologies of video content material figuring out via profiting from task popularity. It hyperlinks a number of well known learn fields in laptop imaginative and prescient, computer studying, human-centered computing, human-computer interplay, picture type, and development acceptance. moreover, the e-book contains a number of key chapters protecting a number of rising issues within the field. Contributed by way of most sensible specialists and practitioners, the chapters current key issues from varied angles and mix either technique and alertness, composing an exceptional assessment of the human task attractiveness concepts.
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Extra info for Human Activity Recognition and Prediction
In: International Conference on Machine Learning (2009) 8. : Behavior recognition via sparse spatiotemporal features. In: Visual Surveillance and Performance Evaluation of Tracking and Surveillance (2005) 9. : Describing objects by their attributes. In: Conference on Computer Vision and Pattern Recognition, pp. 1778–1785. IEEE (2009) 10. : A discriminatively trained, multiscale, deformable part model. In: Conference on Computer Vision and Pattern Recognition (2008) 11. : Learning visual attributes.
Specifically, we introduce a set of binary latent variables for 3D patches indicating which subject the patch is associated with (background, person 1, or person 2), and encourage consistency of the latent variables across all the training data. The appearance and structural information of patches is jointly captured in our model, which captures the motion and pose variations of interacting people. To address the challenge of an exponentially large label space, we use a structured output framework, employing a latent SVM .
In addition, each interaction class associates with a variety of supporting region configurations, thereby providing rich and robust representations for different occlusion cases. We propose a rich representation for close interaction recognition. Specifically, we introduce a set of binary latent variables for 3D patches indicating which subject the patch is associated with (background, person 1, or person 2), and encourage consistency of the latent variables across all the training data. The appearance and structural information of patches is jointly captured in our model, which captures the motion and pose variations of interacting people.
Human Activity Recognition and Prediction by Yun Fu