Distribution Based Crowd Abnormality Detection
Savitha C1, D. Ramesh2

1Savitha C*, Department of ECE , Sri Siddhartha Institute of Technology, Tumakuru, Karnataka, India.
2Dr. D. Ramesh, Department of CSE, Sri Siddhartha Institute of Technology, Tumakuru, Karnataka, India.

Manuscript received on October 18, 2019. | Revised Manuscript received on 29 October, 2019. | Manuscript published on November 10, 2019. | PP: 188-195 | Volume-9 Issue-1, November 2019. | Retrieval Number: A3977119119/2019©BEIESP | DOI: 10.35940/ijitee.A3977.119119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The complications of abnormal behavior and behavior identification are very eminent problems in the video processing. Abnormal behavior detector can be designed by choosing the region of interest through feature detector and by tracking them over the short time period. Therefore, the detector shows the trade-off among the object tracking and optical flow. Since, various regions normally display the various types of motion pattern, we introduce Distribution Based Crowd Abnormality Detection (DCAD) which catches the statistics of object trajectories which are passing via the Spatio-temporal cube. This technique directly provides the distribution to define the frame. Also clustering is not required to build the dictionary. Besides, we exploited the motion trajectories to calculate the “power potentials” in the pixel space which defines the amount of interaction among the people. Furthermore, utilize the standard method for classification by considering SVMs (Support Vector Machines) discriminative learning method to recognize the abnormalities.
Keywords: Detection, Tracking, Template Detector, Distribution Based Crowd Abnormality Detection (DCAD), UMN Dataset.
Scope of the Article: GPS and Location-Based Applications