A Hybrid Method for Human Interaction Recognition Using Spatio-temporal Interest Points

Li, Nijun; Cheng, Xu; Guo, Haiyan; Wu, Zhenyang

doi:10.1109/icpr.2014.434

Cited by 10 publications

(4 citation statements)

References 22 publications

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“…Finally, SVM classifier was trained to recognize testing videos. Li et al [9] incorporated the advantages of both global feature (Motion Context, MC) and SpatioTemporal (S-T) correlation of local Spatio-Temporal Interest Points (STIPs) to describe the human-human interaction actions, and proposed GA search based random forest and S-T correlation based match to achieve better performance of interaction recognition and understanding. This kind of method treats people as a single entity and do not extract the motion of each person from the group.…”

Section: Introductionmentioning

confidence: 99%

A Simple Human Interaction Recognition Based on Global GIST Feature Model

Zuo

Wang

et al. 2015

Intelligent Robotics and Applications

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Abstract. The precision of human interaction recognition mainly relies on the discrimination of action feature descriptors. The descriptors contained global and local information usually can be applied to classify interaction actions. A novel approach is proposed by using the Gist feature model to recognize human interaction actions, which has an advantage of simple feature, easy to operate, good real-time and flexible applications. Taking advantage of the theories with Gaussian pyramid and center-surround mechanism, the gist features from three channels are extracted to represent the human interaction motion, then the classification result is obtained by using frame to frame nearest neighbor classifier and weighted fusion them. The method is tested on UT-Interaction dataset. The experiments show that the method obtained stable performance with simple implementation. Keywords: Interaction recognition · Gist feature · Nearest neighbor classifier · UT-interaction dataset IntroductionHuman interaction recognition and understanding have attracted growing interests in the computer vision community which can be used in intelligent surveillance, video analysis, human-computer interface, etc. Many approaches have been proposed to deal with interaction recognition [1-4]. However, due to the large intra-variations, viewpoint changes [5], clutter and occlusion and the other fundamental factors, which cause the interaction recognition is still a challenging research topic. Many scholars deal with interaction recognition as a general action. This kind of method usually represents the interaction as an integral descriptor including all the people involved in the interaction. Then a classier is utilized to classify interactions Yuan et al. [6] proposed spatio-temporal context to describe local spatio-temporal features and the spatio-temporal relationships between them. Then a spatio-temporal context kernel (STCK) was introduced to recognize human interactions. The feature extraction of this method is simple, however the accuracy of recognition is not good enough. Burghouts et al. [7] improve the performance of this method by exploiting a novel spatio-temporal layout description of interaction, which can improve the discriminative ability of inter-class. Peng et al [8] combined four different features including DT shape, HOG, HOF and MBH to extract low-level feature of multi-scale

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Section: Introductionmentioning

confidence: 99%

A Simple Human Interaction Recognition Based on Global GIST Feature Model

Zuo

Wang

et al. 2015

Intelligent Robotics and Applications

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“…Li et al proposed a hybrid framework which incorporates both a global feature (Motion Context) and a local feature (spatio-temporal interest point (STIP)) to recognize human interactions. The method achieves promising results by respectively using a Genetic Algorithm (GA)-based random forest and calculating the S-T correlation score as the recognition method [11]. As Kinect was introduced by Microsoft, both RGB images and depth images of the scene can be simultaneously captured.…”

Section: Introductionmentioning

confidence: 99%

A New Framework of Human Interaction Recognition Based on Multiple Stage Probability Fusion

Wang

2017

Applied Sciences

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Visual-based human interactive behavior recognition is a challenging research topic in computer vision. There exist some important problems in the current interaction recognition algorithms, such as very complex feature representation and inaccurate feature extraction induced by wrong human body segmentation. In order to solve these problems, a novel human interaction recognition method based on multiple stage probability fusion is proposed in this paper. According to the human body's contact in interaction as a cut-off point, the process of the interaction can be divided into three stages: start stage, execution stage and end stage. Two persons' motions are respectively extracted and recognizes in the start stage and the finish stage when there is no contact between those persons. The two persons' motion is extracted as a whole and recognized in the execution stage. In the recognition process, the final recognition results are obtained by the weighted fusing these probabilities in different stages. The proposed method not only simplifies the extraction and representation of features, but also avoids the wrong feature extraction caused by occlusion. Experiment results on the UT-interaction dataset demonstrated that the proposed method results in a better performance than other recent interaction recognition methods.

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“…al., proposed a hybrid framework which incorporates both global feature and local features to recognize human interactions. The method achieves promising results by respectively using GA based random forest and calculating S-T correlation score as recognition method [13] . This kind of method treats people as a single entity and do not extract the motion of each person from the group.…”

Section: Introductionmentioning

confidence: 99%

“…The most of above methods [8][9]13] choose spatio-temporal interest points as fundamental feature to construct the motion feature for interaction recognition due to their simplicity, effectiveness and robustness to cluttered backgrounds. However interest point always model an action as a bag of independent and order-less visual words without considering the spatio-temporal contextual information of interest points.…”

Section: Introductionmentioning

confidence: 99%