scholarly journals Sifting through the Static: Moving Object Detection in Difference Images

2021 ◽  
Vol 162 (6) ◽  
pp. 245
Author(s):  
Hayden Smotherman ◽  
Andrew J. Connolly ◽  
J. Bryce Kalmbach ◽  
Stephen K. N. Portillo ◽  
Dino Bektesevic ◽  
...  
Keyword(s):  
Solar System ◽  
Object Detection ◽  
Moving Objects ◽  
Graphics Processing Unit ◽  
Moving Object Detection ◽  
Moving Object ◽  
Processing Unit ◽  
Magnitude Distribution ◽  
History Of ◽  
Difference Images

Abstract Trans-Neptunian objects provide a window into the history of the solar system, but they can be challenging to observe due to their distance from the Sun and relatively low brightness. Here we report the detection of 75 moving objects that we could not link to any other known objects, the faintest of which has a VR magnitude of 25.02 ± 0.93 using the Kernel-Based Moving Object Detection (KBMOD) platform. We recover an additional 24 sources with previously known orbits. We place constraints on the barycentric distance, inclination, and longitude of ascending node of these objects. The unidentified objects have a median barycentric distance of 41.28 au, placing them in the outer solar system. The observed inclination and magnitude distribution of all detected objects is consistent with previously published KBO distributions. We describe extensions to KBMOD, including a robust percentile-based lightcurve filter, an in-line graphics-processing unit filter, new coadded stamp generation, and a convolutional neural network stamp filter, which allow KBMOD to take advantage of difference images. These enhancements mark a significant improvement in the readiness of KBMOD for deployment on future big data surveys such as LSST.

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.

scholarly journals Moving Object Detection Using an Object Motion Reflection Model of Motion Vectors

Symmetry ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 34 ◽  
Author(s):  
Jisang Yoo ◽  
Gyu-cheol Lee
Keyword(s):  
Object Detection ◽  
Moving Objects ◽  
Probability Model ◽  
Motion Vector ◽  
Moving Object Detection ◽  
Object Motion ◽  
Moving Object ◽  
Disparity Map ◽  
Motion Vectors ◽  
Reflection Model

Moving object detection task can be solved by the background subtraction algorithm if the camera is fixed. However, because the background moves, detecting moving objects in a moving car is a difficult problem. There were attempts to detect moving objects using LiDAR or stereo cameras, but when the car moved, the detection rate decreased. We propose a moving object detection algorithm using an object motion reflection model of motion vectors. The proposed method first obtains the disparity map by searching the corresponding region between stereo images. Then, we estimate road by applying v-disparity method to the disparity map. The optical flow is used to acquire the motion vectors of symmetric pixels between adjacent frames where the road has been removed. We designed a probability model of how much the local motion is reflected in the motion vector to determine if the object is moving. We have experimented with the proposed method on two datasets, and confirmed that the proposed method detects moving objects with higher accuracy than other methods.

scholarly journals Moving Object Detection and Shadow Removing under Changing Illumination Condition

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Jinhai Xiang ◽  
Heng Fan ◽  
Honghong Liao ◽  
Jun Xu ◽  
Weiping Sun ◽  
...  
Keyword(s):  
Object Detection ◽  
Intensity Ratio ◽  
Moving Objects ◽  
Spatial Relation ◽  
Gaussian Mixture ◽  
Moving Object Detection ◽  
Moving Object ◽  
Illumination Change ◽  
Enhancement Method ◽  
Local Intensity

Moving object detection is a fundamental step in video surveillance system. To eliminate the influence of illumination change and shadow associated with the moving objects, we proposed a local intensity ratio model (LIRM) which is robust to illumination change. Based on the analysis of the illumination and shadow model, we discussed the distribution of local intensity ratio. And the moving objects are segmented without shadow using normalized local intensity ratio via Gaussian mixture model (GMM). Then erosion is used to get the moving objects contours and erase the scatter shadow patches and noises. After that, we get the enhanced moving objects contours by a new contour enhancement method, in which foreground ratio and spatial relation are considered. At last, a new method is used to fill foreground with holes. Experimental results demonstrate that the proposed approach can get moving objects without cast shadow and shows excellent performance under various illumination change conditions.

scholarly journals Moving object detection in the H.264/AVC compressed domain

2016 ◽  
Vol 5 ◽  
Author(s):  
Marcus Laumer ◽  
Peter Amon ◽  
Andreas Hutter ◽  
André Kaup
Keyword(s):  
Object Detection ◽  
Moving Objects ◽  
Spatiotemporal Analysis ◽  
Temporal Analysis ◽  
Detection Algorithm ◽  
Single Step ◽  
Moving Object Detection ◽  
Moving Object ◽  
Compressed Domain ◽  
Video Streams

This paper presents a moving object detection algorithm for H.264/AVC video streams that is applied in the compressed domain. The method is able to extract and analyze several syntax elements from any H.264/AVC-compliant bit stream. The number of analyzed syntax elements depends on the mode in which the method operates. The algorithm is able to perform either a spatiotemporal analysis in a single step or a two-step analysis that starts with a spatial analysis of each frame, followed by a temporal analysis of several subsequent frames. Thereby, in each mode either only (sub-)macroblock types and partition modes or, additionally, quantization parameters are analyzed. The evaluation of these syntax elements enables the algorithm to determine a “weight” for each 4×4 block of pixels that indicates the level of motion within this block. A final segmentation after creating these weights segments each frame to foreground and background and hence indicates the positions and sizes of all moving objects. Our experiments show that the algorithm is able to efficiently detect moving objects in the compressed domain and that it is configurable to process a large number of parallel bit streams in real time.

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.

Comparative Study of Statistical background Modeling and Subtraction

2017 ◽  
Vol 8 (2) ◽  
pp. 287
Author(s):  
Mourad Moussa ◽  
Maha Hmila ◽  
Ali Douik
Keyword(s):  
Object Detection ◽  
Background Subtraction ◽  
Moving Objects ◽  
Computational Cost ◽  
Gaussian Mixture ◽  
Traffic Monitoring ◽  
Human Motion ◽  
Moving Object Detection ◽  
Moving Object ◽  
Dynamic Scenes

Background subtraction methods are widely exploited for moving object detection in videos in many computer vision applications, such as traffic monitoring, human motion capture and video surveillance. The two most distinguishing and challenging aspects of such approaches in this application field are how to build correctly and efficiently the background model and how to prevent the false detection between; (1) moving background pixels and moving objects, (2) shadows pixel and moving objects. In this paper we present a new method for image segmentation using background subtraction. We propose an effective scheme for modelling and updating a background adaptively in dynamic scenes focus on statistical learning. We also introduce a method to detect sudden illumination changes and segment moving objects during these changes. Unlike the traditional color levels provided by RGB sensor aren’t the best choice, for this reason we propose a recursive algorithm that contributes to select very significant color space. Experimental results show significant improvements in moving object detection in dynamic scenes such as waving tree leaves and sudden illumination change, and it has a much lower computational cost compared to Gaussian mixture model.

Improved Spatial-Temporal Moving Object Detection Method Resistant to Noise

2014 ◽  
pp. 46-57
Keyword(s):  
Object Detection ◽  
Moving Objects ◽  
Noise Suppression ◽  
Detection Algorithm ◽  
Moving Object Detection ◽  
Moving Object ◽  
Second Phase ◽  
Moving Objects Detection ◽  
Illumination Changes ◽  
Objects Detection

The resistance of the improved moving objects detection algorithm to various types of additive and multiplicative noise is discussed. The algorithm’s first phase contains the noise suppression filter based on spatiotemporal blocks including dimensionality reduction technique for a compact scalar representation of each block, and the second phase consists of the moving object detection algorithm resistant to illumination changes that detects and tracks moving objects.

scholarly journals Embedded Video Surveillance Based Moving Object Detection and Tracking

2019 ◽  
Vol 8 (3) ◽  
pp. 5740-5745
Keyword(s):  
Object Detection ◽  
Optical Flow ◽  
Video Surveillance ◽  
Intelligent Systems ◽  
Moving Objects ◽  
Moving Object Detection ◽  
Moving Object ◽  
Superior Performance ◽  
Detection And Tracking ◽  
Gradient Based

Background reckoning and the foreground, play prominent roles in the tasks of visual detection and tracking of objects. Moving Object Detection has been widely used in sundry discipline such as intelligent systems, security systems, video monitoring systems, banking places, provisionary systems, and so on. In this paper proposes moving objects detection and tracking method based on Embedded Video Surveillance. The method is based on using lines computed by a gradient-based optical flow and an edge detector gradient-based optical flow and edges are well matched for accurate computation of velocity, not much attention is paid to creating systems for tracking objects using this feature. The proposed method is compared with a recent work, proving its superior performance and when we want to represent high quality videos and images with, lower bit rate, and also suitable for real-world live video applications. This method reduces influences of foreground objects to the background model. The simulation results show that the background image can be obtained precisely and the moving objects recognition is achieved effectively

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