Ant_ViBe: Improved ViBe Algorithm Based on Ant Colony Clustering under Dynamic Background

Yingying Yue,Hao Zhang,Zhiming Qian,Dan Xu,Hongzhen Shi

doi:10.1155/2020/7478626

Abstract

Foreground target detection algorithm (FTDA) is a fundamental preprocessing step in computer vision and video processing. A universal background subtraction algorithm for video sequences (ViBe) is a fast, simple, efficient and with optimal sample attenuation FTDA based on background modeling. However, the traditional ViBe has three limitations: (1) the noise problem under dynamic background; (2) the ghost problem; and (3) the target adhesion problem. In order to solve the three problems above, ant colony clustering is introduced and Ant_ViBe is proposed in this paper to improve the background modeling mechanism of the traditional ViBe, from the aspects of initial sample modeling, pheromone and ant colony update mechanism, and foreground segmentation criterion. Experimental results show that the Ant_ViBe greatly improved the noise resistance under dynamic background, eased the ghost and targets adhesion problem, and surpassed the typical algorithms and their fusion algorithms in most evaluation indexes.

Highlights

Foreground target detection algorithm (FTDA) [1] means to detect moving targets from the video sequence, achieve foreground pixel segmentation and location, provide robust and stable preprocessing results for subsequent advanced tasks such as target tracking, behavior identity, and gesture recognition
Deep learning approach requires more supervision information, manual labeling costs are high, and a lot of repeated training is required when the monitoring scene changes, while traditional methods are unsupervised and with good scenario migration capability. is paper focuses on the traditional method in the field of FTDA. ere are three types of traditional FTDA: interframe difference method, optical flow method, and background modeling method [11, 12]
Stauffer and Grimson proposed Gaussian Mixture Model (GMM) [2] based on statistical models, which regards all gray values of pixels in the video sequence as a random process and assumes that the appearance of gray values follows Gaussian distribution

Summary

Introduction

FTDA [1] means to detect moving targets from the video sequence, achieve foreground pixel segmentation and location, provide robust and stable preprocessing results for subsequent advanced tasks such as target tracking, behavior identity, and gesture recognition. Because Ant_ViBe in this paper introduces the ACCA to foreground target detection problem for the first time, we need to model the background based on ACCA and integrate it into the ViBe framework. Calculate the objective function (3.1.4) Update pheromone matrix (3.2.3) K = K + 1 ACCA Ant_ViBe. Image frame Number of frames. Jt ensures that the background model can be adaptively updated according to the current frame changes, as shown in (8) In this way, global motion that cannot be achieved by a single ant can be achieved, and global segmentation result statistics can be established for each pixel in the time series without increasing the computational complexity, which provides a more robust basis for subsequent segmentation of foreground and background:

Methods

Results

Conclusion