Daily Living Activities Recognition Via Efficient High And Low Level Cues Combination And Fisher Kernel Representation

In this work we propose an efficient method for activity recognition in a daily living scenario. At feature level, we propose a method to extract and combine low- and high-level information and we show that the performance of body pose estimation (and consequently of activity recognition) can be significantly improved. Particularly, we propose an approach extending the pictorial deformable models for the body pose estimation from the state-of-the-art. We show that including low level cues (e. g. optical flow and foreground) together with an off-the-shelf body part detector allows reaching better performance without the need to re-train the detectors. Finally, we apply the Fisher Kernel representation that takes the temporal variation into account and we show that we outperform state-of-the-art methods on a public dataset with daily living activities.