scholarly journals Moving Object Detection under a Moving Camera via Background Orientation Reconstruction

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3103 ◽  
Author(s):  
Wenlong Zhang ◽  
Xiaoliang Sun ◽  
Qifeng Yu
Keyword(s):  
Object Detection ◽  
Moving Objects ◽  
Saliency Map ◽  
Moving Object Detection ◽  
Moving Object ◽  
Reconstruction Method ◽  
Orientation Field ◽  
Moving Camera ◽  
The Difference ◽  
Motion Saliency

Moving object detection under a moving camera is a challenging question, especially in a complex background. This paper proposes a background orientation field reconstruction method based on Poisson fusion for detecting moving objects under a moving camera. As enlightening by the optical flow orientation of a background is not dependent on the scene depth, this paper reconstructs the background orientation through Poisson fusion based on the modified gradient. Then, the motion saliency map is calculated by the difference between the original and the reconstructed orientation field. Based on the similarity in appearance and motion, the paper also proposes a weighted accumulation enhancement method. It can highlight the motion saliency of the moving objects and improve the consistency within the object and background region simultaneously. Furthermore, the proposed method incorporates the motion continuity to reject the false positives. The experimental results obtained by employing publicly available datasets indicate that the proposed method can achieve excellent performance compared with current state-of-the-art methods.

MOVING OBJECT DETECTION FROM VIDEO CAPTURED BY MOVING SURVEILLANCE CAMERA

2021 ◽  
pp. 139-145
Author(s):  
Minh
Keyword(s):  
Object Detection ◽  
Template Matching ◽  
Moving Objects ◽  
Moving Object Detection ◽  
Object Motion ◽  
Moving Object ◽  
Camera Motion ◽  
Moving Camera ◽  
Image Differencing ◽  
Surveillance Camera

This paper presents an effective method for the detection of multiple moving objects from a video sequence captured by a moving surveillance camera. Moving object detection from a moving camera is difficult since camera motion and object motion are mixed. In the proposed method, we created a panoramic picture from a moving camera. After that, with each frame captured from this camera, we used the template matching method to found its place in the panoramic picture. Finally, using the image differencing method, we found out moving objects. Experimental results have shown that the proposed method had good performance with more than 80% of true detection rate on average.

The research of moving object detection with a moving camera

2013 ◽  
Author(s):  
Hai-xin Chen ◽  
Guo-hua Gu ◽  
Xiao-feng Bai ◽  
Tie-kun Zhao ◽  
Fu-yuan Xu
Keyword(s):  
Object Detection ◽  
Moving Object Detection ◽  
Moving Object ◽  
Moving Camera

scholarly journals Optimized Multi-layer Machine Learning Technique for Movement Detection and Tracking in Video Data Stream

2019 ◽  
Vol 8 (10) ◽  
pp. 1151-1159
Keyword(s):  
Object Detection ◽  
Moving Objects ◽  
Error Rates ◽  
Moving Object Detection ◽  
Video Data ◽  
Moving Object ◽  
Detection Accuracy ◽  
Efficient System ◽  
Detection And Tracking ◽  
The Given

With the advent in technology, security and authentication has become the main aspect in computer vision approach. Moving object detection is an efficient system with the goal of preserving the perceptible and principal source in a group. Surveillance is one of the most crucial requirements and carried out to monitor various kinds of activities. The detection and tracking of moving objects are the fundamental concept that comes under the surveillance systems. Moving object recognition is challenging approach in the field of digital image processing. Moving object detection relies on few of the applications which are Human Machine Interaction (HMI), Safety and video Surveillance, Augmented Realism, Transportation Monitoring on Roads, Medical Imaging etc. The main goal of this research is the detection and tracking moving object. In proposed approach, based on the pre-processing method in which there is extraction of the frames with reduction of dimension. It applies the morphological methods to clean the foreground image in the moving objects and texture based feature extract using component analysis method. After that, design a novel method which is optimized multilayer perceptron neural network. It used the optimized layers based on the Pbest and Gbest particle position in the objects. It finds the fitness values which is binary values (x_update, y_update) of swarm or object positions. Method and output achieved final frame creation of the moving objects in the video using BLOB ANALYSER In this research , an application is designed using MATLAB VERSION 2016a In activation function to re-filter the given input and final output calculated with the help of pre-defined sigmoid. In proposed methods to find the clear detection and tracking in the given dataset MOT, FOOTBALL, INDOOR and OUTDOOR datasets. To improve the detection accuracy rate, recall rate and reduce the error rates, False Positive and Negative rate and compare with the various classifiers such as KNN, MLPNN and J48 decision Tree.

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.

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