Abnormal Activity Detection and Notification Platform for Real-Time Ad Hoc Network

Suriya Pinitkan, Nawaporn Wisitpongphan

Abstract


As aging society era is getting near, number of elders who live alone is increasing. These people often need special care. Due to this reason, we propose Abnormal Activity Detection and Notification Platform (AADN) for Real-Time Ad Hoc Network which can help taking care of these people. The proposed platform relies on human tracking using cameras that are installed in different rooms inside the house. AADN will take as input images from the cameras to process and output activity in the form of human pose and objects with their relative distant to the detected human. Relationship Degree of Human Object Interaction (RD-HOI) will be analyzed every minute and be used to distinguish abnormal behavior by means of decision tree. In addition, activities will be used to generate routine behavior log and AADN will notify the person in charge of taking care of the subject if the detected activity differs from the routine. The proposed platform can achieve human pose accuracy of up to 99.66% by using COCO with VGG-NB model and can correctly identify object 68% of the time. Our experiments showed that AADN could notify abnormal activity by using RD-HOI when human and harmful objects were clearly visible in the picture and could correctly notify abnormal activity when time spent in a certain activity differed from the routine by a certain threshold given sufficient amount of data.

Keywords


Human Pose Estimation; Object Detection; Anomaly Detection

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International Journal of Online and Biomedical Engineering (iJOE) – eISSN: 2626-8493
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