This paper presents a framework for recognising realistic human actions captured from unconstrained environments. The novelties of this work lie in three aspects. First, we propos...
Matteo Bregonzio, Jian Li, Shaogang Gong, Tao Xian...
In this paper, a novel feature for capturing information in a spatio-temporal volume based on regularity flow is presented for action recognition. The regularity flow describes ...
In this paper we investigate a discriminative approach to feature weighting for topic identification using minimum classification error (MCE) training. Our approach learns featu...
We present a discriminative part-based approach for human action recognition from video sequences using motion features. Our model is based on the recently proposed hidden conditi...
Recent work shows how to use local spatio-temporal features to learn models of realistic human actions from video. However, existing methods typically rely on a predefined spatial...