01958nas a2200217   4500000000100000000000100001008004100002260001200043653003600055653002500091653001600116653002300132100001600155700002000171245011000191856009600301300001000397490000600407520131300413022001401726       2019                            d  c06/201910aBody posture recognition system10aActivity recognition10aSmartCities10aPattern Clustering1 aAhmad Jalal1 aShaharyar Kamal00aImproved Behavior Monitoring and Classification Using Cues Parameters Extraction from Camera Array Images  uhttps://www.ijimai.org/journal/sites/default/files/files/2018/07/ijimai_5_5_9_pdf_48446.pdf  a71-780 v53 aBehavior 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.  a1989-1660