Moving object area detection using normalized self adaptive optical flow - Sengar, Sandeep Singh; Mukhopadhyay, Susanta (Optik (Stuttgart), 2016)
Optical flow estimation is one of the oldest and still most active research domains in computer vision. This paper proposes a novel and efficient method of moving object area detection in the video sequence employing the normalized self-adaptive optical flow. This new approach first performs smoothing on the individual frame of the video data using Gaussian filter, then determines the optical flow field with an existing optical flow algorithm, next filters out the noise using adaptive threshold approach, after that normalize, morphology operation, and the self adaptive window approach is applied to identify the moving object areas. The proposed work is accurate for detecting the moving object areas with varying object size. The proposed scheme has been formulated, implemented and tested on real video data sets that provides an effective and efficient way in a complex background environment.