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Start Over Descriptor "human silhouette" Topic action recognition
2 results on '"human silhouette"'

1. Action recognition using graph embedding and the co-occurrence matrices descriptor.

Author

Zheng, Feng, Shao, Ling, Song, Zhan, and Chen, Xi

Subjects
*EMBEDDINGS (Mathematics), *GRAPH theory, *MATRICES (Mathematics), *COMPUTER vision, *ALGORITHMS, *NONLINEAR statistical models
Abstract

Recognizing actions from a monocular video is a very hot topic in computer vision recently. In this paper, we propose a new representation of actions, the co-occurrence matrices descriptor, on the intrinsic shape manifold learned by graph embedding. The co-occurrence matrices descriptor captures more temporal information than the bag of words (histogram) descriptor which only considers the spatial information, thus boosting the classification accuracy. In addition, we compare the performance of the co-occurrence matrices descriptor on different manifolds learned by various graph-embedding methods. Graph-embedding methods preserve as much of the significant structure of the high-dimensional data as possible in the low-dimensional map. The results show that nonlinear algorithms are more robust than linear ones. Furthermore, we conclude that the label information plays a critical role in learning more discriminating manifolds. [ABSTRACT FROM AUTHOR]

Published
2011
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2. Histogram of oriented rectangles: A new pose descriptor for human action recognition

Author

Pinar Duygulu and Nazlı İkizler

Subjects
Complex representation, Dynamic time warping, Computation time, Computation, Human Motion Understanding, Temporal representations, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Compact representation, Histogramming, Temporal domain, k-nearest neighbors algorithm, Silhouette, Human silhouette, Nearest neighbor classification, Gesture recognition, Motion energy, Statistical tests, Models, Histogram, Parameter estimation, Computer vision, Robust recognition, Pose Descriptor, Mathematics, Action sequences, Accuracy rate, business.industry, Spatial domains, Pattern recognition, Descriptor, Human-action recognition, Rectangular patch, Support vector machine, Human form models, Complex model, Action Recognition, Signal Processing, Computer Vision and Pattern Recognition, Artificial intelligence, Data sets, business, Classifier (UML), Human pose
Abstract

Cataloged from PDF version of article. Most of the approaches to human action recognition tend to form complex models which require lots of parameter estimation and computation time. In this study, we show that, human actions can be simply represented by pose without dealing with the complex representation of dynamics. Based on this idea, we propose a novel pose descriptor which we name as Histogram-of-Oriented-Rectangles (HOR) for representing and recognizing human actions in videos. We represent each human pose in an action sequence by oriented rectangular patches extracted over the human silhouette. We then form spatial oriented histograms to represent the distribution of these rectangular patches. We make use of several matching strategies to carry the information from the spatial domain described by the HOR descriptor to temporal domain. These are (i) nearest neighbor classification, which recognizes the actions by matching the descriptors of each frame, (ii) global histogramming, which extends the idea of Motion Energy Image proposed by Bobick and Davis to rectangular patches, (iii) a classifier-based approach using Support Vector Machines, and (iv) adaptation of Dynamic Time Warping on the temporal representation of the HOR descriptor. For the cases when pose descriptor is not sufficiently strong alone, such as to differentiate actions "jogging" and "running", we also incorporate a simple velocity descriptor as a prior to the pose based classification step. We test our system with different configurations and experiment on two commonly used action datasets: the Weizmann dataset and the KTH dataset. Results show that our method is superior to other methods on Weizmann dataset with a perfect accuracy rate of 100%, and is comparable to the other methods on KTH dataset with a very high success rate close to 90%. These results prove that with a simple and compact representation, we can achieve robust recognition of human actions, compared to complex representations. (C) 2009 Elsevier B.V. All rights reserved.

Published
2009

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