An evaluation of optical flow algorithms for crowd analytics in surveillance system

Kajo, I. and Malik, A.S. and Kamel, N. (2017) An evaluation of optical flow algorithms for crowd analytics in surveillance system. In: UNSPECIFIED.

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Abstract

Optical flow technique is one of the significant motion estimation techniques. Due to its importance, several optical flow technique have been used in order to estimate the velocity and the direction of the pedestrians in the crowded scenes. This paper presents an overview of the optical flow methods that used mainly for pedestrian and crowd motion detection. The work focuses on the conventional optical flow method such as Lucas & Kanade and Horn & Schunck methods as well as the most recent methods such as Classic+NL that combines the classic formulation with a new non-local term. The improvement in computational efficiency and increasing interest in robust and accurate motion estimation algorithms lead to increase in the use of optical flow in crowd analytic applications. The implementation of optical flow algorithms is investigated and an evaluation of those techniques is provided qualitatively as well as quantitatively. The qualitative analysis illustrates the optical flow performance in terms of large motion, occlusion, motion discontinuities, illumination and different light condition. Quantitative analysis is in terms of computational time and accuracy. © 2016 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: cited By 11; Conference of 6th International Conference on Intelligent and Advanced Systems, ICIAS 2016 ; Conference Date: 15 August 2016 Through 17 August 2016; Conference Code:125970
Uncontrolled Keywords: Computational efficiency; Motion analysis; Motion estimation, Crowd; Motion detection; Motion Vectors; Object velocity and direction; Optical flow estimation, Optical flows
Depositing User: Mr Ahmad Suhairi UTP
Date Deposited: 09 Nov 2023 16:20
Last Modified: 09 Nov 2023 16:20
URI: https://khub.utp.edu.my/scholars/id/eprint/8924

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