An Unsupervised Approach for Human Activity Detection and Recognition

Author(s):  
Wee Hong Ong ◽  
Leon Palafox ◽  
Takafumi Koseki
Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 5001 ◽  
Author(s):  
Zhendong Zhuang ◽  
Yang Xue

As an active research field, sport-related activity monitoring plays an important role in people’s lives and health. This is often viewed as a human activity recognition task in which a fixed-length sliding window is used to segment long-term activity signals. However, activities with complex motion states and non-periodicity can be better monitored if the monitoring algorithm is able to accurately detect the duration of meaningful motion states. However, this ability is lacking in the sliding window approach. In this study, we focused on two types of activities for sport-related activity monitoring, which we regard as a human activity detection and recognition task. For non-periodic activities, we propose an interval-based detection and recognition method. The proposed approach can accurately determine the duration of each target motion state by generating candidate intervals. For weak periodic activities, we propose a classification-based periodic matching method that uses periodic matching to segment the motion sate. Experimental results show that the proposed methods performed better than the sliding window method.


Author(s):  
Stevan Cakic ◽  
Stevan Sandi ◽  
Daliborka Nedic ◽  
Srdan Krco ◽  
Tomo Popovic

Author(s):  
Harshit Grover ◽  
Dheryta Jaisinghani ◽  
Nishtha Phutela ◽  
Shivani Mittal

2020 ◽  
Vol 8 (6) ◽  
pp. 3949-3953

Nowadays there is a significant study effort due to the popularity of CCTV to enhance analysis methods for surveillance videos and video-based images in conjunction with machine learning techniques for the purpose of independent assessment of such information sources. Although recognition of human intervention in computer vision is extremely attained subject, abnormal behavior detection is lately attracting more research attention. In this paper, we are interested in the studying the two main steps that compose abnormal human activity detection system which are the behavior representation and modelling. And we use different techniques, related to feature extraction and description for behavior representation as well as unsupervised classification methods for behavior modelling. In addition, available datasets and metrics for performance evaluation will be presented. Finally, this paper will be aimed to detect abnormal behaved object in crowd, such as fast motion in a crowd of walking people


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