Background Removal Using RGB-D Data for Fright Recognition
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2016, Vol 4, Issue 9
Abstract
This research paper evaluates the automatic recognition of various human activities while moving inside a lobby using depth data for background removal and using the surrounding walls as reference lines of frame. The study examines detection of panic and fear as the subject of focus in the process of activity recognition. Many times, humans escape from danger and threat, try to evade, run to a secure spot because of fear caused by ambush, violence, presence of life threatening incidents such as shooting or because of natural calamity such as cyclone, hurricane, flash floods, earthquake and tornado. The research first uses a sequence of image to extract the human blob, shape form using image filtering. A background to foreground subtraction approach is taken to eliminate non-interesting regions. The blob, shape form is then normalized using a reference frame of lobby, room, followed by edge enhancements and then features are extracted by repeated application of a mesh with varying thresholds. Finally, a support vector machine (SVM) classifier was used to detect activity that represented fright. The results showed recognition accuracy of 74.8% for continuous, automatic, real time unconstrained video image series.
Authors and Affiliations
Amol S Patwardhan
Keywords
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