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This paper proposes an innovative and effective hybrid way to recognize human interactions, which incorporates the advantages of both global feature (Motion Context, MC) and Spatio-Temporal (S-T) correlation of local Spatio-TemporalInterest Points (STIPs). The MC feature, which also derives from STIPs, is used to train a random forest where Genetic Algorithm (GA) is applied to the training phase to achieve a good compromise between reliability and efficiency. Besides, we design an effective and efficient S-T correlation based match to assist the MC feature, where MC's structure and a biological sequence matching algorithm are employed to calculate the spatial and temporal correlation score, respectively. Experiments on the UTInteraction dataset show that our GA search based random forest and S-T correlation based match achieve better performance than some other prevalent machine leaning methods, and that a combination of those two methods outperforms most of the state-of-the-art works.

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SUMMARYThis paper proposes a robust superpixel-based tracker via multiple-instance learning, which exploits the importance of instances and mid-level features captured by superpixels for object tracking. We first present a superpixels-based appearance model, which is able to compute the confidences of the object and background. Most importantly, we introduce the sample importance into multiple-instance learning (MIL) procedure to improve the performance of tracking. The importance for each instance in the positive bag is defined by accumulating the confidence of all the pixels within the corresponding instance. Furthermore, our tracker can help recover the object from the drifting scene using the appearance model based on superpixels when the drift occurs. We retain the first (k − 1) frames' information during the updating process to alleviate drift to some extent. To evaluate the effectiveness of the proposed tracker, six video sequences of different challenging situations are tested. The comparison results demonstrate that the proposed tracker has more robust and accurate performance than six ones representing the state-of-the-art.