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

TitleImproved Behavior Monitoring and Classification Using Cues Parameters Extraction from Camera Array Images
Publication TypeJournal Article
Year of Publication2019
AuthorsJalal, A., and S. Kamal
JournalInternational Journal of Interactive Multimedia and Artificial Intelligence
ISSN1989-1660
IssueRegular Issue
Volume5
Number5
Date Published06/2019
Pagination71-78
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.

KeywordsActivity recognition, Body posture recognition system, Pattern Clustering, SmartCities
DOI10.9781/ijimai.2017.07.003
URLhttps://www.ijimai.org/journal/sites/default/files/files/2018/07/ijimai_5_5_9_pdf_48446.pdf
AttachmentSize
ijimai_5_5_9.pdf560.17 KB