Feature Selection for Tracker-Less Human Activity Recognition

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.