Lying-pose detection with training dataset expansion

We propose a rotation and scale invariant method to locate people lying on the ground. Unlike conventional human-shape detection methods which assume that all human shapes are in upright position, a person lying on the ground can have arbitrary orientation and pose. Accounting for every possible body configuration would thus require a huge training dataset that would be challenging to gather. In this paper, we propose a method which increases the size of a small training dataset and allows to detect multiple body poses. To do so, our method increases the size of the dataset with a geometric distortion method followed by a rejection sampling method. Then, it automatically identifies K body configurations in the training set, realign it in upright position and trains K SVM classifiers, one for each body configuration. Lying pose detection is then performed by considering a max pooling strategy across all K SVM classifiers.