Application of improved firefly optimization algorithm in motion shadow removal

Liu Lei; Cao Min; Zhang Xiao

doi:10.12086/oee.2019.180120

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Liu Lei, Cao Min, Zhang Xiao. Application of improved firefly optimization algorithm in motion shadow removal[J]. Opto-Electronic Engineering, 2019, 46(4): 180120. doi: 10.12086/oee.2019.180120

Citation:

Liu Lei, Cao Min, Zhang Xiao. Application of improved firefly optimization algorithm in motion shadow removal[J]. Opto-Electronic Engineering, 2019, 46(4): 180120. doi: 10.12086/oee.2019.180120

Application of improved firefly optimization algorithm in motion shadow removal

School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China

Fund Project: Supported by National Major Instrument and Equipment Development Special (2016YFF0101402) and Shanghai University Young Teachers Training Program Funded Projects (ZZsl15008)

More Information

Corresponding author: Cao Min, E-mail: mincao@usst.edu.cn

Received Date 13 March 2018

Revised Date 21 December 2018

Published Date 01 April 2019

Abstract

Abstract

The motion shadow is conglutinous with the object, and has the consistency of motion. It is often misdetected as a part of the moving target. The existence of motion shadow changes the shape of the moving object and influences the further analysis of the foreground of the moving target. To solve this problem, a motion shadow removal algorithm based on improved firefly optimization algorithm is proposed. The optimal threshold is obtained by optimizing the 2-Otsu distance measure function based on the improved glowworm algorithm which is based on the influence of the best position in the population history, and then the image is segmented and the moving shadow is removed. Compared our method with the traditional 2-Otsu method, particle swarm optimization (PSO) optimize 2-Otsu method, firefly optimization algorithm (FA) optimize 2-Otsu method, the experimental results show that the algorithm are 2.69, 1.42 and 1.21 times faster than the other three methods in the presence of shadow. Besides, it is superior to the other three algorithms in terms of region consistency, shadow detection rate and recognition rate. The effectiveness of the method is verified.
- motion shadow /
- improved firefly optimization algorithm /
- the best threshold /
- image segmentation /
- 2-Otsu method

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References

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Overview

Overview

Overview: Moving shadow removal is an important research field in computer vision. The purpose is to eliminate the influence of moving shadow on the foreground of moving objects, so as to extract the foreground of moving objects accurately. The moving shadow has similar motion characteristics with the object, often is detected as part of the foreground of the moving target. The geometric characteristics such as the shape and the center of mass of moving objects will appear errors due to the existence of shadows, which will affect the measurement, location, tracking and segmentation of moving objects. It increases the difficulty of moving object detection, and has a bad effect on subsequent recognition and tracking. Currently, shadow removal methods are mainly based on color and texture. The method based on color information uses the invariance of shadows to remove the shadows in the target detection results. However, one disadvantage of this method is that they can only detect small shadows in a frame, and are insensitive to the recognition and shadow detection of dark-tone targets. Texture-based methods use the texture similarity features of shadows to remove the shadow areas, but there are some defects in dealing with flat areas and similar foreground and background textures. Although there are many shadow removal algorithms, the existing algorithms are only applicable to a specific target or a specific application scenario, and the output of the algorithm needs to be improved. To solve this problem, a moving shadow removal algorithm based on improved glowworm optimization algorithm is proposed. That is, the optimal threshold is obtained by optimizing the 2-Otsu distance measure function based on the improved glowworm algorithm which is based on the influence of the best position in the population history, and then the image is segmented and the moving shadow is removed. The results are compared with the traditional 2-Otsu method, particle swarm optimization 2-Otsu method and firefly optimization 2-Otsu method. Experimental results show that this method is 2.69 times, 1.42 times, and 1.21 times faster than the other three methods. The improved algorithm not only has fewer parameters, simpler operation, better stability and global optimization effect, but also has faster convergence speed. The improved firefly algorithm solves the problems of the 2-Otsu method, such as large amount of calculation, high computational complexity and poor real-time performance. Compared with the other three shadow removal algorithms, the algorithm in this paper has been effectively improved in terms of time, region consistency, shadow detection efficiency, detection accuracy and target integrity. Experiments show that the algorithm improves shadow removal efficiency and algorithm speed.