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Even if the problem of human action categorization from videos has received a lot of attention during the past decade, it remains a challenging problem in operative conditions due to camera motion, occlusion, moving background, illumination changes and the variations of human appearance and postures. In this paper a new motion descriptor, based on a sparse optical flow computed by interest point tracking...
In this paper we evaluate the use of Restricted Bolzmann Machines (RBM) in the context of learning and recognizing human actions. The features used as basis are binary silhouettes of persons. We test the proposed approach on two datasets of human actions where binary silhouettes are available: ViHASi (synthetic data) and Weizmann (real data). In addition, on Weizmann dataset, we combine features based...
This paper presents a method for estimating geographic location for sequences of time-stamped photographs. A prior distribution over travel describes the likelihood of traveling from one location to another during a given time interval. This distribution is based on a training database of 6 million photographs from Flickr.com. An image likelihood for each location is defined by matching a test photograph...
A gait recognition algorithm is proposed that fuses motion and static features of sequences of silhouette images - the wavelet moment and the widths capture the motion and static characteristic of gait. A subspace transformation, principal component analysis (PCA), is applied to process the spatial templates. It aims essentially at reducing data dimensionalities. Finally, nearest neighbor classifier...
Graphical models have proved to be very efficient models for labeling image data. In particular, they have been used to label data samples from human body images. In this paper, a DTG-based graphical model is studied for human-body landmark localization and tracking along the image sequence. Experimental results on human motion databases are shown.
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