Statistics of Pairwise Co-occurring Local Spatio-Temporal Features for Human Action Recognition

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In the next section, the filtration performances are given in terms of permeation flux, lactic acid / lactate recovery rate, impurities rejection (phosphate, sulfate,

In this paper, we propose a new approach based on utilization of motion boundaries to generate Motion Stable Shape (MSS) features to describe human actions in videos.. In fact, we

To overcome the huge variability of human actions in motion and appearance, we propose a supervised approach to learn local motion descriptors – actlets – from a large pool of

We deal with cases of Lambertian surfaces, called cases 1 - 4 (Figure 21), with their BRDF which is the natural reectance behavior of the surface (Figure 22), and their ABRDF

• Spatio-temporal SURF (ST-SURF): After video temporal segmentation and human action detection, we propose a new algorithm based on the extension of the SURF descriptors to the

Aggarwal, “View invariant human action recognition using histograms of 3d joints,” in Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 IEEE Computer Society

Using two benchmark datasets for human action recognition, we demonstrate that our representation enhances the discriminative power of features and improves action