Improved Behavior Monitoring and Classification Using Cues Parameters Extraction from Camera Array Images

Improved Behavior Monitoring and Classification Using Cues Parameters Extraction from Camera Array Images

Behavior monitoring and classification is a mechanism used to automatically identify or verify individual based on their human detection, tracking and behavior recognition from video sequences captured by a depth camera. In this paper, we designed a system that precisely classifies the nature of 3D...

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Journal Title: International Journal of Interactive Multimedia and Artificial Intelligence
First author: Ahmad Jalal
Other Authors: Shaharyar Kamal
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Language: Undetermined
Get full text: https://www.ijimai.org/journal/sites/default/files/files/2018/07/ijimai_5_5_9_pdf_48446.pdf
https://www.ijimai.org/journal/node/2501
Resource type: Journal Article
Source: International Journal of Interactive Multimedia and Artificial Intelligence; Vol 5, No 5 (Year 2019).
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Publisher: Universidad Internacional de La Rioja
Usage rights: Reconocimiento (by)
Subjects: Physical/Engineering Sciences --> Computer Science, Artificial Intelligence
Abstract: Behavior monitoring and classification is a mechanism used to automatically identify or verify individual based on their human detection, tracking and behavior recognition from video sequences captured by a depth camera. In this paper, we designed a system that precisely classifies the nature of 3D body postures obtained by Kinect using an advanced recognizer. We proposed novel features that are suitable for depth data. These features are robust to noise, invariant to translation and scaling, and capable of monitoring fast human bodyparts movements. Lastly, advanced hidden Markov model is used to recognize different activities. In the extensive experiments, we have seen that our system consistently outperforms over three depth-based behavior datasets, i.e., IM-DailyDepthActivity, MSRDailyActivity3D and MSRAction3D in both posture classification and behavior recognition. Moreover, our system handles subject's body parts rotation, self-occlusion and body parts missing which significantly track complex activities and improve recognition rate. Due to easy accessible, low-cost and friendly deployment process of depth camera, the proposed system can be applied over various consumer-applications including patient-monitoring system, automatic video surveillance, smart homes/offices and 3D games.