Recognize human activities from multi-part missing videos

Recognizing human activities from multi-part missing videos is a challenge problem. When the multiple missing parts are continuous, the problem is reduced to activity recognition in videos with single part missing at any position which is focused on by many researches. However, in many practical applications, some temporal gaps always appear in captured videos due to random frame loss(e.g. noise interfere). To solve this problem, we propose a novel framework: 1) dividing each video into multiple equal-length segments, where the local spatio-temporal features extracted; 2) concatenating combinatorial sparse activity dictionaries, formed by over-complete dictionary of each segment; 3) computing combinatorial sparse coefficients of each segment, based on activity dictionaries above; 4) formulating probability of each activity to estimate the correct class. Our experiments achieve superior performance not only in videos with single part missing at any position, but also in videos with multiple parts missing.