Moving Object Detection in Video Under Dynamic Background Condition Using Block-Based Statistical Features

2017 ◽  
pp. 371-383
Author(s):  
Amlan Raychaudhuri ◽  
Satyabrata Maity ◽  
Amlan Chakrabarti ◽  
Debotosh Bhattacharjee
Keyword(s):  
Object Detection ◽  
Moving Object Detection ◽  
Moving Object ◽  
Statistical Features ◽  
Background Condition ◽  
Dynamic Background ◽  
Block Based

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.

Moving object detection based on optical flow and neural network fusion

2016 ◽  
Vol 9 (4) ◽  
pp. 325-335 ◽  
Author(s):  
Haiqun Qin ◽  
Ziyang Zhen ◽  
Kun Ma
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Optical Flow ◽  
Target Detection ◽  
Moving Object Detection ◽  
Moving Object ◽  
Monitoring Systems ◽  
Dynamic Background ◽  
Content Type ◽  
New Generation

Purpose The purpose of this paper is to meet the large demand for the new-generation intelligence monitoring systems that are used to detect targets within a dynamic background. Design/methodology/approach A dynamic target detection method based on the fusion of optical flow and neural network is proposed. Findings Simulation results verify the accuracy of the moving object detection based on optical flow and neural network fusion. The method eliminates the influence caused by the movement of the camera to detect the target and has the ability to extract a complete moving target. Practical implications It provides a powerful safeguard for target detection and targets the tracking application. Originality/value The proposed method represents the fusion of optical flow and neural network to detect the moving object, and it can be used in new-generation intelligent monitoring systems.

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.

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
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