Recognising human actions by analysing negative spaces

The authors propose a novel region-based method to recognise human actions. Other region-based approaches work on silhouette of the human body, which is termed as the positive space according to art theory. In contrast, the authors investigate and analyse regions surrounding the human body, termed as the negative space for human action recognition. This concept takes advantage of the naturally formed negative regions that come with simple shape, simplifying the job for action classification. Negative space is less sensitive to segmentation errors, overcoming some limitations of silhouette-based methods such as leaks or holes in the silhouette caused by background segmentation. Inexpensive semantic-level description can be generated from the negative space that supports fast and accurate action recognition. The proposed system has obtained 100% accuracy on the Weizmann human action dataset and the robust sequence dataset. On KTH dataset the system achieved 94.67% accuracy. Furthermore, 95% accuracy can be achieved even when half of the negative space regions are ignored. This makes our work robust with respect to segmentation errors and distinctive from other approaches.The authors propose a novel region-based method to recognise human actions. Other region-based approaches work on silhouette of the human body, which is termed as the positive space according to art theory. In contrast, the authors investigate and analyse regions surrounding the human body, termed as the negative space for human action recognition. This concept takes advantage of the naturally formed negative regions that come with simple shape, simplifying the job for action classification. Negative space is less sensitive to segmentation errors, overcoming some limitations of silhouette-based methods such as leaks or holes in the silhouette caused by background segmentation. Inexpensive semantic-level description can be generated from the negative space that supports fast and accurate action recogn- tion. The proposed system has obtained 100% accuracy on the Weizmann human action dataset and the robust sequence dataset. On KTH dataset the system achieved 94.67% accuracy. Furthermore, 95% accuracy can be achieved even when half of the negative space regions are ignored. This makes our work robust with respect to segmentation errors and distinctive from other approaches.