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

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.