Online Detection of Long-Term Daily Living Activities by Weakly Supervised Recognition of Sub-Activities

In this paper, we address detection of activities in long-term untrimmed videos. Detecting temporal delineation of activities is important to analyze large-scale videos. However, there are still challenges yet to be overcome in order to have an accurate temporal segmentation of activities. Detection of daily-living activities is even more challenging due to their high intra-class and low inter-class variations, complex temporal relationships of sub-activities performed in realistic settings. To tackle these problems, we propose an online activity detection framework based on the discovery of sub-activities. We consider a long-term activity as a sequence of short-term sub-activities. Then we utilize a weakly supervised classifier trained on discovered sub-activities which allows us to predict an ongoing activity before being completely observed. To achieve a more precise segmentation a greedy post-processing technique based on Markov models is employed. We evaluate our framework on DAHLIA and GAADRD daily living activity datasets where we achieve state-of-the-art results on detection of activities.