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

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Yingying Yue ◽  
Dan Xu ◽  
Zhiming Qian ◽  
Hongzhen Shi ◽  
Hao Zhang
Keyword(s):  
Video Processing ◽  
Detection Algorithm ◽  
Background Modeling ◽  
Ant Colony ◽  
Noise Resistance ◽  
Initial Sample ◽  
Dynamic Background ◽  
Evaluation Indexes ◽  
Ant Colony Clustering ◽  
Vibe Algorithm

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.

scholarly journals WePBAS: A Weighted Pixel-Based Adaptive Segmenter for Change Detection

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2672
Author(s):  
Wenhui Li ◽  
Jianqi Zhang ◽  
Ying Wang
Keyword(s):  
Change Detection ◽  
High Weight ◽  
State Of The Art ◽  
Diffusion Mechanism ◽  
Background Modeling ◽  
Background Model ◽  
Dynamic Background ◽  
Low Weight ◽  
Model Update ◽  
Sample Set

The pixel-based adaptive segmenter (PBAS) is a classic background modeling algorithm for change detection. However, it is difficult for the PBAS method to detect foreground targets in dynamic background regions. To solve this problem, based on PBAS, a weighted pixel-based adaptive segmenter named WePBAS for change detection is proposed in this paper. WePBAS uses weighted background samples as a background model. In the PBAS method, the samples in the background model are not weighted. In the weighted background sample set, the low-weight background samples typically represent the wrong background pixels and need to be replaced. Conversely, high-weight background samples need to be preserved. According to this principle, a directional background model update mechanism is proposed to improve the segmentation performance of the foreground targets in the dynamic background regions. In addition, due to the “background diffusion” mechanism, the PBAS method often identifies small intermittent motion foreground targets as background. To solve this problem, an adaptive foreground counter was added to the WePBAS to limit the “background diffusion” mechanism. The adaptive foreground counter can automatically adjust its own parameters based on videos’ characteristics. The experiments showed that the proposed method is competitive with the state-of-the-art background modeling method for change detection.

scholarly journals Strategy for Foreground Movement Identification Adaptive to Background Variations

2018 ◽  
Vol 8 (6) ◽  
pp. 4258
Author(s):  
K. Anuradha ◽  
N.R. Raajan
Keyword(s):  
Video Processing ◽  
Video Sequences ◽  
Surveillance Video ◽  
Dynamic Background ◽  
Public Places ◽  
Frame Difference ◽  
F Measure ◽  
State Of Art ◽  
Art Techniques

<p>Video processing has gained a lot of significance because of its applications in various areas of research. This includes monitoring movements in public places for surveillance. Video sequences from various standard datasets such as I2R, CAVIAR and UCSD are often referred for video processing applications and research. Identification of actors as well as the movements in video sequences should be accomplished with the static and dynamic background. The significance of research in video processing lies in identifying the foreground movement of actors and objects in video sequences. Foreground identification can be done with a static or dynamic background. This type of identification becomes complex while detecting the movements in video sequences with a dynamic background. For identification of foreground movement in video sequences with dynamic background, two algorithms are proposed in this article. The algorithms are termed as Frame Difference between Neighboring Frames using Hue, Saturation and Value (FDNF-HSV) and Frame Difference between Neighboring Frames using Greyscale (FDNF-G). With regard to F-measure, recall and precision, the proposed algorithms are evaluated with state-of-art techniques. Results of evaluation show that, the proposed algorithms have shown enhanced performance.</p>

An Analysis of Ant Colony Clustering Methods: Models, Algorithms and Applications

2011 ◽  
Vol 3 (11) ◽  
pp. 112-121 ◽  
Author(s):  
Gong Zhe ◽  
Li Dan ◽  
An Baoyu ◽  
Ou Yangxi ◽  
Cui Wei ◽  
...  
Keyword(s):  
Ant Colony ◽  
Clustering Methods ◽  
Ant Colony Clustering

A Multi-Robot System Using Mobile Agents with Ant Colony Clustering

2010 ◽  
pp. 174-192
Author(s):  
Yasushi Kambayashi ◽  
Yasuhiro Tsujimura ◽  
Hidemi Yamachi ◽  
Munehiro Takimoto
Keyword(s):  
Communication Networks ◽  
Mobile Agents ◽  
Software Agents ◽  
Ant Colony ◽  
Multiple Robots ◽  
Robot System ◽  
Mobile Software ◽  
Robot Systems ◽  
Ant Colony Clustering ◽  
Multi Robot

This chapter presents a framework using novel methods for controlling mobile multiple robots directed by mobile agents on a communication networks. Instead of physical movement of multiple robots, mobile software agents migrate from one robot to another so that the robots more efficiently complete their task. In some applications, it is desirable that multiple robots draw themselves together automatically. In order to avoid excessive energy consumption, we employ mobile software agents to locate robots scattered in a field, and cause them to autonomously determine their moving behaviors by using a clustering algorithm based on the Ant Colony Optimization (ACO) method. ACO is the swarm-intelligence-based method that exploits artificial stigmergy for the solution of combinatorial optimization problems. Preliminary experiments have provided a favorable result. Even though there is much room to improve the collaboration of multiple agents and ACO, the current results suggest a promising direction for the design of control mechanisms for multi-robot systems. In this chapter, we focus on the implementation of the controlling mechanism of the multi-robot system using mobile agents.

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