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Detection of Static and Dynamic Abnormal Activities in Crowded Areas Using Hybrid Clustering

  • M. R. Sumalatha
  • P. Lakshmi Harika
  • J. Aravind
  • S. Dhaarani
  • P. Rajavi
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)

Abstract

In Computer Vision, to monitor the activities, behavior, and other changing information, surveillance is used. Surveillance is used by government organizations, private companies for security purposes. In video processing, anomaly is generally considered as a rarely occurring event. In a crowded area, it is impossible to monitor the occasionally moving objects and each person’s behavior. The main objective is to design a framework that detects the occasionally moving objects and abnormal human activities in the video. Histogram of Oriented Gradient feature extraction is used for the detection of an occasionally moving object and abnormality detection involves in computing the motion map by the flow of motion vectors in a scene that detects the change in movement. The experimental analysis demonstrates the effectiveness of this approach which is efficient to run in real time achieves 96% performance, however, for effective validation of the system is tested with standard UMN datasets and own datasets.

Keywords

Hybrid clustering Occasionally moving object detection Uncommon human activity detection 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Information TechnologyMIT Campus, Anna UniversityChennaiIndia

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