Publication Type:

Book Chapter

Source:

Lecture Notes in Computational Vision and Biomechanics, Book Chapter-2019, Springer Netherlands, Volume 30, p.955-964 (2019)

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060257913&doi=10.1007%2f978-3-030-00665-5_92&partnerID=40&md5=82692cfd7f99aec973675e83368d7f2f

Abstract:

Locality Sensitive Hashing (LSH) is an approach which is extensively used for comparing document similarity. In our work, this technique is incorporated in a video environment for finding dissimilarity between the frames in the video so as to detect motion. This has been implemented for a single point camera archiving, wherein the images are converted into pixel file using a rasterization procedure. Pixels are then tokenized and hashed using minhashing procedure which employs a randomized algorithm to quickly estimate the Jaccard similarity. LSH finds the dissimilarity among the frames in the video by breaking the minhashes into a series of band comprising of rows. The proposed procedure is implemented on multiple datasets, and from the experimental analysis, we infer that it is capable of isolating the motions in a video file.

Notes:

cited By 0

Cite this Research Publication

M. Srenithi and Dr. (Col.) Kumar P. N., “Motion Detection Algorithm for Surveillance Videos”, in Lecture Notes in Computational Vision and Biomechanics, Book Chapter-2019, vol. 30, Springer Netherlands, 2019, pp. 955-964.