Automatic moving object segmentation methods under varying illumination conditions for video data: comparative study, and an improved method

摘要

In recent past, many moving object segmentation methods under varying lighting changes have been proposed in literature and each of them has their own benefits and limitations. The various methods available in literature for moving object segmentation may be broadly classified into four categories i.e., moving object segmentation methods based on (i) motion information (ii) motion and spatial information (iii) learning (iv) and change detection. The objective of this paper is two-fold i.e., firstly, this paper presents a comprehensive comparative study of various classical as well as state-of-the art methods for moving object segmentation under varying illumination conditions under each of the above mentioned four categories and secondly this paper presents an improved approximation filter based method in complex wavelet domain and its comparison with other methods under four categories mentioned as above. The proposed approach consist of seven steps applied on given video frames which include: wavelet decomposition of frames using Daubechies complex wavelet transform; use of improved approximate median filter on detail co-efficient (LH, HL, HH); use of background modeling on approximate co-efficient (LL sub-band); soft thresholding for noise removal; strong edge detection; inverse wavelet transformation for reconstruction; and finally using closing morphology operator. The qualitative and quantitative comparative study of the various methods under four categories as well as the proposed method is presented for six different datasets. The merits, demerits, and efficacy of each of the methods under consideration have been examined. The extensive experimental comparative analysis on six different challenging benchmark data sets demonstrate that proposed method is performing better to other state-of-the-art moving object segmentation methods and is well capable of dealing with various limitations of existing methods.