Mid-level features and spatio-temporal context for activity recognition

In this paper, we extend the Spatio-Temporal Graph Convolutional Network for skeleton- based action recognition by introducing two novel modules, namely, the Graph Vertex

Our representation captures not only global distribution of features but also focuses on geometric and appearance (both visual and motion) relations among the fea- tures.. Calculating

Contrary to the state-of-the-art methods implying procedures to extract feature vectors (mean, variance, skewness, kurtosis...) for all acceleration signals and then reduce

Appearance and flow informa- tion is extracted at characteristic positions obtained from human pose and aggregated over frames of a video.. Our P-CNN description is shown to

Abstract— In robotic navigation, biologically inspired local- ization models have often exhibited interesting features and proven to be competitive with other solutions in terms

A Comparison of Wavelet Based Spatio-temporal Decomposition Methods for Dynamic Texture Recognition... Spatio-temporal Decomposition Methods for Dynamic

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• 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