Community Detection in Complex Network Based on APT Method

Automatically detecting human-object interaction by an instance part-level attention deep framework

Effective abnormal human behavior analysis serves as a warning signal before emergencies. However, most abnormal human behavior detections rely on manual monitoring at present. This method is criticized for being subjective and lack of timeliness. In response to the problems above, this paper proposes a multistage analysis method of abnormal human behavior in complex scenes. This paper firstly differentiates the abnormal behavior roughly from a large monitoring area with similarity measurement applied to the social force model, and precise analysis is conducted thereafter. The multistage analysis, based on the three-frame difference algorithm, is used for intrusion, left-behind baggage detection, and motion trajectory identification. The experimental result demonstrates the superiority of the proposed method in UMV, CAVIAR, and datasets. To demonstrate the adaptability and generalization ability of the proposed method, this paper selects the CVC and JAAD driving anomaly detection data sets to test the method. Experimental results show that the proposed method is superior to the existing methods.

/pdf/multistage-analysis-of-abnormal-human-behavior-in-complex-3hw99p28t8.pdf

Multistage Analysis of Abnormal Human Behavior in Complex Scenes

Editorial learning for multimodal data

Analysis of the trajectory shapes of moving objects in the video sequence with use of structural description

Visual phrase recognition by modeling 3D spatial context of multiple objects

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Visual phrase recognition by modeling 3D spatial context of multiple objects