Dense Trajectories and Motion Boundary Descriptors for Action Recognition
نویسندگان
چکیده
منابع مشابه
Motion boundary based sampling and 3D co-occurrence descriptors for action recognition
Recent studies witness the success of Bag-of-Features (BoF) frameworks for video based human action recognition. The detection and description of local interest regions are two fundamental problems in BoF framework. In this paper, we propose a motion boundary based sampling strategy and spatialtemporal (3D) co-occurrence descriptors for action video representation and recognition. Our sampling ...
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The most important problem in action recognition is how to represent an action video. The approaches can be roughly divided into four categories: (1) human pose based approaches which utilize human structure information; (2) global action template based approaches which capture appearance and motion information on the whole motion body; (3) local feature based approaches which mainly extract va...
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In this paper, we propose a novel approach to extract local descriptors of a video, based on two ideas, one using motion boundary between objects, and, second, the resulting motion boundary trajectories extracted from videos, together with other local descriptors in the neighbourhood of the extracted motion boundary trajectories, histogram of oriented gradients, histogram of optical flow, motio...
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With recent improved dense trajectory features (HOG, warped HOF, and warped MBH), we employ two advanced super vector methods, namely Fisher Vector (FV) and soft Vector of Locally Aggregated Descriptors (VLAD-K) to encode them separately. The two individual super vectors are concatenated into a Hybrid Super Vector, and a linear SVM classifier is used to predict labels. We achieve 87.46%1 in ave...
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ژورنال
عنوان ژورنال: International Journal of Computer Vision
سال: 2013
ISSN: 0920-5691,1573-1405
DOI: 10.1007/s11263-012-0594-8