Publication Type:

Book Chapter

Source:

Lecture Notes in Computational Vision and Biomechanics, Springer Netherlands, Volume 30, p.615-629 (2019)

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060208655&doi=10.1007%2f978-3-030-00665-5_61&partnerID=40&md5=3291350aa3de0d29bdaffcc14cfc5221

Abstract:

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. © Springer Nature Switzerland AG 2019.

Notes:

cited By 0

Cite this Research Publication

R. Manjusha and Dr. Latha Parameswaran, “Design of an image skeletonization based algorithm for overcrowd detection in smart building”, in Lecture Notes in Computational Vision and Biomechanics, vol. 30, Springer Netherlands, 2019, pp. 615-629.