Section: New Results

Human Action Recognition in Videos

Participants : Piotr Bilinski, Etienne Corvée, Slawomir Bak, François Brémond.

keywords: action recognition, tracklets, head detection, relative tracklets, bag-of-words.

In this work we address the problem of recognizing human actions in video sequences for home care applications.

Recent studies have shown that approaches which use a bag-of-words representation reach high action recognition accuracy. Unfortunately, these approaches have problems to discriminate similar actions, ignoring spatial information of features.

We propose a feature representation for action recognition based on dense point tracklets, head position estimation, and a dynamic coordinate system. Our main idea is that action recognition ought to be performed using a dynamic coordinate system corresponding to an object of interest. Therefore, we introduce a relative tracklet descriptor based on relative positions of a tracklet according to the central point of our dynamic coordinate system. As a center of our dynamic coordinate system, we choose the head position, providing description invariant to camera viewpoint changes. We use the bag-of-words approach to represent a video sequence and we capture global distribution of tracklets and relative tracklet descriptors over a video sequence. The proposed descriptors introduce spatial information to the bag-of-words model and help to distinguish similar features detected at different positions (e.g. to distinguish similar features appearing on hands and feet). Then we apply the Support Vector Machines with exponential chi-squared kernel to classify videos and recognize actions.

We report experimental results on three action recognition datasets (publicly available KTH and ADL datasets, and our locally collected dataset). Our locally collected dataset has been created in cooperation with the CHU Nice Hospital. It contains people performing daily living activities such as: standing up, sitting down, walking, reading a magazine, etc. Consistently, experiments show that our representation enhances the discriminative power of the tracklet descriptors and the bag-of-words model, and improves action recognition performance.

Sample video frames with extracted tracklets and estimated head positions are presented in Figure 30 .

This work has been published in [32] .

Acknowledgments

This work was supported by the Région Provence-Alpes-Côte d'Azur. However, the views and opinions expressed herein do not necessarily reflect those of the financing institution