A Multiscale Parametric Background Model for Stationary Foreground Object Detection

2007 ◽  
Author(s):  
Steven Cheng ◽  
Xingzhi Luo ◽  
Suchendra Bhandarkar
Keyword(s):  
Object Detection ◽  
Background Model ◽  
Foreground Object

scholarly journals Real-time Foreground Object Detection Combining the PBAS Background Modelling Algorithm and Feedback from Scene Analysis Module

2014 ◽  
Vol 60 (1) ◽  
pp. 53-64 ◽  
Author(s):  
Tomasz Kryjak ◽  
Mateusz Komorkiewicz ◽  
Marek Gorgon
Keyword(s):  
Object Detection ◽  
Detection Algorithm ◽  
Scene Analysis ◽  
Foreground Object ◽  
Connected Component ◽  
Hardware Platform ◽  
Static Object ◽  
Consecutive Frame ◽  
Sobel Edge Detection ◽  
Edge Similarity

Abstract The article presents a hardware implementation of the foreground object detection algorithm PBAS (Pixel-Based Adaptive Segmenter) with a scene analysis module. A mechanism for static object detection is proposed, which is based on consecutive frame differencing. The method allows to distinguish stopped foreground objects (e.g. a car at the intersection, abandoned luggage) from false detections (so-called ghosts) using edge similarity. The improved algorithm was compared with the original version on popular test sequences from the changedetection.net dataset. The obtained results indicate that the proposed approach allows to improve the performance of the method for sequences with the stopped objects. The algorithm has been implemented and successfully verified on a hardware platform with Virtex 7 FPGA device. The PBAS segmentation, consecutive frame differencing, Sobel edge detection and advanced one-pass connected component analysis modules were designed. The system is capable of processing 50 frames with a resolution of 720 × 576 pixels per second

scholarly journals Moving Object Detection for Dynamic Background Scenes Based on Spatiotemporal Model

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Yizhong Yang ◽  
Qiang Zhang ◽  
Pengfei Wang ◽  
Xionglou Hu ◽  
Nengju Wu
Keyword(s):  
Object Detection ◽  
Moving Objects ◽  
Moving Object Detection ◽  
Moving Object ◽  
Background Model ◽  
Dynamic Background ◽  
Spatiotemporal Model ◽  
Temporal Features ◽  
Common Technique ◽  
Public Datasets

Moving object detection in video streams is the first step of many computer vision applications. Background modeling and subtraction for moving detection is the most common technique for detecting, while how to detect moving objects correctly is still a challenge. Some methods initialize the background model at each pixel in the first N frames. However, it cannot perform well in dynamic background scenes since the background model only contains temporal features. Herein, a novel pixelwise and nonparametric moving object detection method is proposed, which contains both spatial and temporal features. The proposed method can accurately detect the dynamic background. Additionally, several new mechanisms are also proposed to maintain and update the background model. The experimental results based on image sequences in public datasets show that the proposed method provides the robustness and effectiveness in dynamic background scenes compared with the existing methods.

Research on the Algorithm of Key Object Extraction in Motion Video Images

2011 ◽  
Vol 186 ◽  
pp. 541-545
Author(s):  
Zhong Qu ◽  
Wei Wei ◽  
Zhen Wei Zhang ◽  
Dong Wang
Keyword(s):  
Edge Detection ◽  
Object Detection ◽  
Object Extraction ◽  
Foreground Object ◽  
Original Image ◽  
Detection And Tracking ◽  
Video Images ◽  
Object Edge ◽  
Simulation Results ◽  
Motion Video

In this paper, we extract for key objects of motion video images (mainly people), then do researches with the technology of object detection and tracking. First, we extract moving object edge and foreground object template with the use of the method of edge detection, and then compare the resulting object template with the original image, thus dye the positions with the colour of the corresponding point coordinates in the original image which belongs to the extent of template filled region. Simulation results show that the proposed algorithm has some advantages and robustness and could meet project needs.

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