Authors: Simone Calderara, Andrea Prati, Rita Cucchiara
Addresses: Dipartimento di Ingegneria dell'Informazione, University of Modena and Reggio Emilia, Via Vignolese, 905/b – 41122 Modena, Italy. ' Dipartimento di Scienze e Metodi dell'Ingegneria, University of Modena and Reggio Emilia, Via Amendola, 2 – Pad. Morselli – 42122 Reggio Emilia, Italy. ' Dipartimento di Ingegneria dell'Informazione, University of Modena and Reggio Emilia, Via Vignolese, 905/b – 41122 Modena, Italy
Abstract: This paper presents a method for recognising human actions by tracking body parts without using artificial markers. A sophisticated appearance-based tracking able to cope with occlusions is exploited to extract a probability map for each moving object. A segmentation technique based on mixture of Gaussians (MoG) is then employed to extract and track significant points on this map, corresponding to significant regions on the human silhouette. The evolution of the mixture in time is analysed by transforming it in a sequence of symbols (corresponding to a MoG). The similarity between actions is computed by applying global alignment and dynamic programming techniques to the corresponding sequences and using a variational approximation of the Kullback-Leibler divergence to measure the dissimilarity between two MoGs. Experiments on publicly available datasets and comparison with existing methods are provided.
Keywords: action recognition; mean tracking; mixture of Gaussians; MoG; dynamic programming; body parts tracking; human actions; global alignment.
International Journal of Multimedia Intelligence and Security, 2010 Vol.1 No.1, pp.76 - 89
Published online: 11 Oct 2010 *Full-text access for editors Access for subscribers Purchase this article Comment on this article