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Traffic density analysis employing locality sensitive hashing on GPS data and image processing techniques

Publication Type : Book Chapter

Publisher : Lecture Notes in Computational Vision and Biomechanics

Source : Lecture Notes in Computational Vision and Biomechanics, Springer Netherlands, Volume 28, p.959-971 (2018)

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Campus : Coimbatore

School : School of Engineering

Center : Amrita Innovation & Research

Department : Computer Science

Verified : Yes

Year : 2018

Abstract : Recent development of GPS enabled devices helps in tracking the approximate location of any device. Any GPS enabled device with working internet can be tracked at any point of time. The data obtained from GPS serves several purposes, such as tracking lost devices, providing directions to a certain destination, etc. In several public environments, difficulty arises in plugging the rescue operation during any emergency needs. In case of traffic, the raw data about the traffic closure will not help the authority to reach right location. Instead, the information such as, near accurate location and time of traffic collected from GPS helps the authority to reach the destination. In this paper location of vehicle crowd formation is detected by applying similarity detection with locality sensitive hashing to the collected GPS data and two approaches with LSH (on numerical computation and on image processing) are proposed. The LSH technique is used to hash the location data to find the vehicles at similar locations and time. The proposed approaches are lightweight and needs less computational effort, since dual hashing is employed thus making it suitable for real time applications. This paper uses Apache spark for detecting vehicle crowd by applying LSH to the GPS data, since it is very fast and handles enormous data. Notes: cited By 0

Cite this Research Publication : K. Sowmya and Dr. (Col.) Kumar P. N., “Traffic Density Analysis Employing Locality Sensitive Hashing on GPS Data and Image Processing Techniques”, in Lecture Notes in Computational Vision and Biomechanics, vol. 28, Springer Netherlands, 2018, pp. 959-971.

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