Detecting moving objects from dynamic background combining subspace learning with mixed norm approach

2020 ◽  
Vol 79 (25-26) ◽  
pp. 18747-18766
Author(s):  
Yuqiu Lu ◽  
Jingjing Liu ◽  
Wang Liu ◽  
Shiwei Ma ◽  
Xianchao Xiu ◽  
...  
Keyword(s):  
Moving Objects ◽  
Subspace Learning ◽  
Mixed Norm ◽  
Dynamic Background

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.

scholarly journals Moving Object Detection Based on Background Compensation and Deep Learning

Symmetry ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 1965
Author(s):  
Juncai Zhu ◽  
Zhizhong Wang ◽  
Songwei Wang ◽  
Shuli Chen
Keyword(s):  
Object Detection ◽  
Moving Objects ◽  
Spatial Information ◽  
Difficult Problem ◽  
Moving Object Detection ◽  
Moving Object ◽  
Dynamic Background ◽  
Background Compensation ◽  
Overall Performance ◽  
Temporal And Spatial

Detecting moving objects in a video sequence is an important problem in many vision-based applications. In particular, detecting moving objects when the camera is moving is a difficult problem. In this study, we propose a symmetric method for detecting moving objects in the presence of a dynamic background. First, a background compensation method is used to detect the proposed region of motion. Next, in order to accurately locate the moving objects, we propose a convolutional neural network-based method called YOLOv3-SOD for detecting all objects in the image, which is lightweight and specifically designed for small objects. Finally, the moving objects are determined by fusing the results obtained by motion detection and object detection. Missed detections are recalled according to the temporal and spatial information in adjacent frames. A dataset is not currently available specifically for moving object detection and recognition, and thus, we have released the MDR105 dataset comprising three classes with 105 videos. Our experiments demonstrated that the proposed algorithm can accurately detect moving objects in various scenarios with good overall performance.

scholarly journals Automatic foreground detection based on KDE and binary classification

2019 ◽  
Vol 15 (1) ◽  
pp. 517
Author(s):  
Mohammed Lahraichi ◽  
Khalid Housni ◽  
Samir Mbarki
Keyword(s):  
Moving Objects ◽  
Binary Classification ◽  
Video Sequences ◽  
Maximum A Posteriori ◽  
A Posteriori ◽  
Dynamic Scene ◽  
Dynamic Background ◽  
New Approach ◽  
Markov Random ◽  
Global Threshold

In the recent decades, several methods have been developed to extract moving objects in the presence of dynamic background. However, most of them use a global threshold, and ignore the correlation between neighboring pixels. To address these issues, this paper presents a new approach to generate a probability image based on Kernel Density Estimation (KDE) method, and then apply the Maximum A Posteriori in the Markov Random Field (MAP-MRF) based on probability image, so as to generate an energy function, this function will be minimized by the binary graph cut algorithm to detect the moving pixels instead of applying a thresholding step. The proposed method was tested on various video sequences, and the obtained results showed its effectiveness in presence of a dynamic scene, compared to other background subtraction models.

scholarly journals Segmentation of Moving Objects using Numerous Background Subtraction Methods for Surveillance Applications

2020 ◽  
Vol 9 (3) ◽  
pp. 2553-2563
Keyword(s):  
Background Subtraction ◽  
Moving Objects ◽  
Tracking System ◽  
Real Life ◽  
Ground Truth ◽  
Computational Time ◽  
Dynamic Background ◽  
Illumination Changes ◽  
Benchmark Datasets ◽  
Surveillance Applications

Background subtraction is a key part to detect moving objects from the video in computer vision field. It is used to subtract reference frame to every new frame of video scenes. There are wide varieties of background subtraction techniques available in literature to solve real life applications like crowd analysis, human activity tracking system, traffic analysis and many more. Moreover, there were not enough benchmark datasets available which can solve all the challenges of subtraction techniques for object detection. Thus challenges were found in terms of dynamic background, illumination changes, shadow appearance, occlusion and object speed. In this perspective, we have tried to provide exhaustive literature survey on background subtraction techniques for video surveillance applications to solve these challenges in real situations. Additionally, we have surveyed eight benchmark video datasets here namely Wallflower, BMC, PET, IBM, CAVIAR, CD.Net, SABS and RGB-D along with their available ground truth. This study evaluates the performance of five background subtraction methods using performance parameters such as specificity, sensitivity, FNR, PWC and F-Score in order to identify an accurate and efficient method for detecting moving objects in less computational time.

Moving Objects Detection Based on Gaussian Mixture Model and Saliency Map

2011 ◽  
Vol 63-64 ◽  
pp. 350-354 ◽  
Author(s):  
Li Li Lin ◽  
Neng Rong Chen
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Moving Objects ◽  
Visual Saliency ◽  
Gaussian Mixture ◽  
Saliency Map ◽  
Dynamic Background ◽  
Moving Objects Detection ◽  
Saliency Maps ◽  
Objects Detection

The background modeling method based on the Gaussian mixture model (GMM) is usually used to detect the moving objects in static background. But when applied to dynamic background, for example caused by camera jitter, the wrong detection rate of moving objects is high, and thus affects the follow-up tracking. In addition, the method with GMM can not effectively remove the moving objects shadow region. This paper proposes a moving object detection method based on GMM and visual saliency maps, which not only can remove the disturbance caused by camera jitter, but also can effectively solve the shadow problem and achieve stable moving objects detection.

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