Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
A Roadmap to the Integration of Early Visual Modules
International Journal of Computer Vision
A survey on vision-based human action recognition
Image and Vision Computing
Survey of Pedestrian Detection for Advanced Driver Assistance Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised and Online Update of Boosted Temporal Models: The UAL2Boost
ICMLA '10 Proceedings of the 2010 Ninth International Conference on Machine Learning and Applications
Human detection using oriented histograms of flow and appearance
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
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We address the empirical feature selection for tracker-less recognition of human actions. We rely on the appearance plus motion model over several video frames to model the human movements. We use the L2Boost algorithm, a versatile boosting algorithm which simplifies the gradient search. We study the following options in the feature computation and learning: (i) full model vs. component-wise model, (ii) sampling strategy of the histogram cells and (iii) number of previous frames to include, amongst others. We select the features' parameters that provide the best compromise between performance and computational efficiency and apply the features in a challenging problem, the tracker-less and detection-less human activity recognition.