Abstract
In this paper, we focus on the recognition and localization of human interactions in real-world videos. It is a difficult challenge because of large variations in person appearance, camera viewpoint, length of video, intra-class variability, and etc. To address these challenges, we present a spatial structure model in this paper. In our model, the crucial movement of each category is represented using a segment of the entire video. To capture the spatial configuration of the human interactions within the video segment, a spatial structure model is built over the segment, and trajectory features are extracted within each cell. The proposed model is trained automatically from real-world videos that are annotated only with the classification label. We examine our approach on the TVHI dataset, which contain 4 complex human interaction action classes. The experimental results demonstrate the effectiveness of our model.
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Wu, J., Chen, F., Hu, D. (2013). Human Interaction Recognition by Spatial Structure Models. In: Sun, C., Fang, F., Zhou, ZH., Yang, W., Liu, ZY. (eds) Intelligence Science and Big Data Engineering. IScIDE 2013. Lecture Notes in Computer Science, vol 8261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42057-3_28
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DOI: https://doi.org/10.1007/978-3-642-42057-3_28
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