Memetic algorithms: a short introduction
New ideas in optimization
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Efficient Visual Event Detection Using Volumetric Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Image Understanding
Human action recognition by feature-reduced Gaussian process classification
Pattern Recognition Letters
Multiple and variable target visual tracking for video-surveillance applications
Pattern Recognition Letters
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Silhouette-based human action recognition using sequences of key poses
Pattern Recognition Letters
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Visual recognition of human actions in image sequences is an active field of research. However, most recent published methods use complex models and heuristics of the human body as well as to classify their actions. Our approach follows a different strategy. It is based on simple feature extraction from descriptors obtained from a visual tracking system. The tracking system is able to bring some useful information like position and size of the subject at every time step of a sequence, and in this paper we show that, the evolution of some of these features is enough to classify an action in most of the cases.