Object Detection with Adaptive Background Model and Margined Sign Cross Correlation

2006 ◽  
Author(s):  
H. Yoshimura ◽  
Y. Iwai ◽  
M. Yachida
Keyword(s):  
Object Detection ◽  
Cross Correlation ◽  
Background Model

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.

A Modified Gaussian Mixture Background Model for Moving Object Detection

2017 ◽  
Vol 14 (8) ◽  
pp. 3672-3678 ◽  
Author(s):  
Mansoor Ahmed Khuhro ◽  
Dongjun Huang ◽  
Shaonian Huang ◽  
Papias Niyigena ◽  
Ammar Oad
Keyword(s):  
Object Detection ◽  
Gaussian Mixture ◽  
Moving Object Detection ◽  
Moving Object ◽  
Background Model

Moving object detection zone using a block‐based background model

2017 ◽  
Vol 12 (1) ◽  
pp. 86-94 ◽  
Author(s):  
Omar Elharrouss ◽  
Abdelghafour Abbad ◽  
Driss Moujahid ◽  
Hamid Tairi
Keyword(s):  
Object Detection ◽  
Moving Object Detection ◽  
Moving Object ◽  
Background Model ◽  
Detection Zone ◽  
Block Based

Vehicle Flow Detection Using Fast Region Matching with Adaptive Gaussian Mixture Background Model

2010 ◽  
Vol 108-111 ◽  
pp. 1272-1277
Author(s):  
Hui Ding
Keyword(s):  
Cross Correlation ◽  
Gaussian Mixture ◽  
Background Model ◽  
Large Set ◽  
Moving Vehicle ◽  
Video Clips ◽  
Region Matching ◽  
Recursive Computation ◽  
Flow Detection ◽  
Fast Region

Video surveillance play an important role in many ITS. In this paper, we present a fast and reliable algorithm for detecting traffic flow count. The core of the algorithm relies on Gaussian mixture background model combined fast cross-correlation region-based techniques in moving object matching. By working with adaptive Gaussian mixture model, obtained the moving vehicle as foreground. Then, fast local correlation, referred to as single matching phase, is achieved by using recursive computation schemes, which enabled us to minimize the amount of calculations required at every new pixel. We have tested our match algorithm in a large set of experiments with video clips and achieved good matching results.

Detecting and Tracking Segmentation of Moving Objects Using Graph Cut Algorithm

2018 ◽  
pp. 177-192
Author(s):  
Raviraj Pandian ◽  
Ramya A.
Keyword(s):  
Object Detection ◽  
Moving Objects ◽  
Graph Cut ◽  
Background Model ◽  
Camera Motion ◽  
Current Frame ◽  
Outdoor Environments ◽  
Previous Frame ◽  
Indoor And Outdoor Environments ◽  
Graph Cut Segmentation

Real-time moving object detection, classification, and tracking capabilities are presented with system operates on both color and gray-scale video imagery from a stationary camera. It can handle object detection in indoor and outdoor environments and under changing illumination conditions. Object detection in a video is usually performed by object detectors or background subtraction techniques. The proposed method determines the threshold automatically and dynamically depending on the intensities of the pixels in the current frame. In this method, it updates the background model with learning rate depending on the differences of the pixels in the background model of the previous frame. The graph cut segmentation-based region merging algorithm approaches achieve both segmentation and optical flow computation accurately and they can work in the presence of large camera motion. The algorithm makes use of the shape of the detected objects and temporal tracking results to successfully categorize objects into pre-defined classes like human, human group, and vehicle.

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