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In this paper, we propose a framework to recognize complex human interactions. First, we adopt trajectories to represent human motion in a video. Then, the extracted trajectories are clustered into different groups (named as local motion patterns) using the coherent filtering algorithm. As trajectories within the same group exhibit similar motion properties (i.e., velocity, direction), we adopt the histogram of large-displacement optical flow (denoted as HO-LDOF) as the group motion feature vector. Thus, each video can be briefly represented by a collection of local motion patterns that are described by the HO-LDOF. Finally, classification is achieved using the citation-KNN, which is a typical multiple-instance-learning algorithm. Experimental results on the TV human interaction dataset and the UT human interaction dataset demonstrate the applicability of our method.

In this article, we present a novel approach to segment discriminative patches in human activity videos. First, we adopt the spatio-temporal interest points (STIPs) to represent significant motion patterns in the video sequence. Then, nonnegative sparse coding is exploited to generate a sparse representation of each STIP descriptor. We construct the feature vector for each video by applying a two-stage sum-pooling and l 2 -normalization operation. After training a multi-class classifier through the error-correcting code SVM, the discriminative portion of each video is determined as the patch that has the highest confidence while also being correctly classified according to the video category. Experimental results show that the video patches extracted by our method are more separable, while preserving the perceptually relevant portion of each activity.