Design of an Image Skeletonization Based Algorithm for Overcrowd Detection in Smart Building

  • R. ManjushaEmail author
  • Latha Parameswaran
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)


Crowd analysis has found its significance in varied applications from security purposes to commercial use. This proposed algorithm aims at contour extraction from skeleton of the foreground image for identifying and counting people and for providing crowd alert in the given scene. The proposed algorithm is also compared with other conventional algorithms like HoG with SVM classifier, Haar cascade and Morphological Operator. Experimental results show that the proposed method aids better crowd analysis than the other three algorithms on varied datasets with varied illumination and varied concentration of people.


People counting HoG SVM Haar Kalman filter Morphological operators Skeletonization 



The proposed work is tested on three different datasets. (i) Mall dataset [16, 17]; (ii) Dataset from surveillance camera of Amrita Vishwa Vidyapeetham, the university with which we work and where the proposed work is carried on; (iii) User defined video, which is video of myself on which the proposed work was tested upon.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringAmrita School of EngineeringCoimbatoreIndia

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