Improved Behavior Monitoring and Classification Using Cues Parameters Extraction from Camera Array Images
2018
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.
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