scholarly journals Moving Object Detection Using Ultrasonic Radar with Proper Distance, Direction, and Object Shape Analysis

2020 ◽  
Vol 6 (2) ◽  
pp. 99
Author(s):  
Angona Biswas ◽  
Sabrina Abedin ◽  
Md. Ahasan Kabir
Keyword(s):  
Moving Objects ◽  
Radar System ◽  
Moving Object ◽  
Small Object ◽  
Large Set ◽  
Object Shape ◽  
Proper Distance ◽  
Radio Detection ◽  
Different Shapes ◽  
The Military

Background: In its early development, radar (radio detection and ranging) was primarily used by the navy, the military, and the aviation services, as well as space organizations for security and monitoring purposes. Nowadays, the demand of radar is expanding. Research has been conducted to overcome the limitations of radar.Objective: One of the current limitations to detect moving object. The current paper aims to fill the gap in the literature by using a radar system in the identification of moving object, capturing the distance, direction, radar pulse duration and object shape simultaneously. Velocity or the object’s speed towards or away from the radar was determined by using an algorithm to obtain the precision.Methods: The accuracy of distance measurement and angle is ensured by comparing the real values and the values obtained by the radar. The objects under study consist of metal and non-metal. Novelty of this work is the accurate detection of moving objects with suitable algorithms using only one Arduino UNO and one ultrasonic sensor.Results: The experiment design yielded much better efficiency than previous works. The proposed method predicted the exact speed of the object detected by the radar system. The experiment has successfully proven the accuracy of moving object sensor.Conclusion: Besides proper distance and velocity, a large set of data was taken to find the accuracy of the radar for objects of different shapes. For a cylindrical object, the radar provided 100% efficiency in a constant environment when the object was 5 cm away. The accuracy decreased to 30% when the distance was 17 cm away. The limitation of this system is that it was unable to detect small object or if the object was very close (1 cm).

scholarly journals U2-ONet: A Two-Level Nested Octave U-Structure Network with a Multi-Scale Attention Mechanism for Moving Object Segmentation

2020 ◽  
Vol 13 (1) ◽  
pp. 60
Author(s):  
Chenjie Wang ◽  
Chengyuan Li ◽  
Jun Liu ◽  
Bin Luo ◽  
Xin Su ◽  
...  
Keyword(s):  
Moving Objects ◽  
Object Segmentation ◽  
Contextual Information ◽  
Attention Mechanism ◽  
Moving Object ◽  
Feature Maps ◽  
Moving Object Segmentation ◽  
Practical Applications ◽  
Multi Scale ◽  
Spatial Redundancy

Most scenes in practical applications are dynamic scenes containing moving objects, so accurately segmenting moving objects is crucial for many computer vision applications. In order to efficiently segment all the moving objects in the scene, regardless of whether the object has a predefined semantic label, we propose a two-level nested octave U-structure network with a multi-scale attention mechanism, called U2-ONet. U2-ONet takes two RGB frames, the optical flow between these frames, and the instance segmentation of the frames as inputs. Each stage of U2-ONet is filled with the newly designed octave residual U-block (ORSU block) to enhance the ability to obtain more contextual information at different scales while reducing the spatial redundancy of the feature maps. In order to efficiently train the multi-scale deep network, we introduce a hierarchical training supervision strategy that calculates the loss at each level while adding knowledge-matching loss to keep the optimization consistent. The experimental results show that the proposed U2-ONet method can achieve a state-of-the-art performance in several general moving object segmentation datasets.

Influence of Ball and Background Patterns on Perception of Visual Direction of a Moving Object

1979 ◽  
Vol 49 (2) ◽  
pp. 343-346 ◽  
Author(s):  
Marcella V. Ridenour
Keyword(s):  
Analysis Of Variance ◽  
Moving Objects ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Moving Object ◽  
Visual Direction ◽  
Visual Patterns ◽  
Prediction Time ◽  
The Subject ◽  
Three Dimensional Space

30 boys and 30 girls, 6 yr. old, participated in a study assessing the influence of the visual patterns of moving objects and their respective backgrounds on the prediction of objects' directionality. An apparatus was designed to permit modified spherical objects with interchangeable covers and backgrounds to move in three-dimensional space in three directions at selected speeds. The subject's task was to predict one of three possible directions of an object: the object either moved toward the subject's midline or toward a point 18 in. to the left or right of the midline. The movements of all objects started at the same place which was 19.5 ft. in front of the subject. Prediction time was recorded on 15 trials. Analysis of variance indicated that visual patterns of the moving object did not influence the prediction of the object's directionality. Visual patterns of the background behind the moving object did not influence the prediction of the object's directionality except during the conditions of a light nonpatterned moving object. It was concluded that visual patterns of the background and that of the moving object have a very limited influence on the prediction of direction.

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.

Improved Illumination Independent Moving Object Detection Algorithm

2014 ◽  
pp. 15-22
Keyword(s):  
Real World ◽  
Moving Objects ◽  
Image Sequence ◽  
Detection Algorithm ◽  
Moving Object Detection ◽  
Moving Object ◽  
Detection Technique ◽  
Real Image ◽  
Illumination Changes ◽  
Precision And Accuracy

A real time change detection technique is proposed in order to detect the moving objects in a real image sequence. The described method is independent of the illumination of the analyzed scene. It is based on a comparison of corresponding pixels that belong to different frames and combines time and space analysis, which augments the algorithm’s precision and accuracy. The efficiency of the described technique is illustrated on a real world interior video sequence recorded under significant illumination changes.

A Survey of Spatio-Temporal Data Warehousing

2010 ◽  
pp. 949-977
Author(s):  
Leticia Gómez ◽  
Bart Kuijpers ◽  
Bart Moelans ◽  
Alejandro Vaisman
Keyword(s):  
Moving Objects ◽  
Route Planning ◽  
Geographic Information ◽  
Moving Object ◽  
Attribute Data ◽  
Object Databases ◽  
Spatio Temporal ◽  
Eu Project ◽  
The University ◽  
Moving Object Databases

Geographic Information Systems (GIS) have been extensively used in various application domains, ranging from economical, ecological and demographic analysis, to city and route planning. Nowadays, organizations need sophisticated GIS-based Decision Support System (DSS) to analyze their data with respect to geographic information, represented not only as attribute data, but also in maps. Thus, vendors are increasingly integrating their products, leading to the concept of SOLAP (Spatial OLAP). Also, in the last years, and motivated by the explosive growth in the use of PDA devices, the field of moving object data has been receiving attention from the GIS community. However, not much has been done in providing moving object databases with OLAP functionality. In the first part of this article we survey the SOLAP literature. We then move to Spatio-Temporal OLAP, in particular addressing the problem of trajectory analysis. We finally provide an in-depth comparative analysis between two proposals introduced in the context of the GeoPKDD EU project: the Hermes-MDC system, and Piet, a proposal for SOLAP and moving objects, developed at the University of Buenos Aires, Argentina.

Image Identification of a Moving Object Based on an Improved Canny Edge Detection Algorithm

2018 ◽  
Author(s):  
Yang Liu ◽  
Lingyu Sun ◽  
Lijun Li ◽  
Yiben Zhang ◽  
Zongmiao Dai ◽  
...  
Keyword(s):  
Edge Detection ◽  
Moving Objects ◽  
Median Filter ◽  
Critical Role ◽  
Detection Algorithm ◽  
Object Identification ◽  
Moving Object ◽  
Detection Accuracy ◽  
Image Process ◽  
Canny Operator

Edge detection plays an increasingly critical role in image process community, especially for moving object identification problems. For this case, the target object can be captured straightly via the edges beside which there is an obvious jump of grey value or texture. Nowadays, Canny operator has gained great popularity as it shows higher anti-noise performance and presents better detection accuracy in comparison with other edge detection operators like Robert’s, Sobel’s, Prewitt’s etc. However, the Gaussian filter associated with the classic Canny operator is sometimes too simple to decrease the all-type-noise. Additionally, in order to enhance the detection accuracy and lower the pseudo-edges detection ratio, two thresholds, high and low, are chosen artificially which have actually limited the adaptability of the algorithm. In this work, a compound filter, Gaussian-Median filter, is proposed to improve the smoothing effect. The self-adaptive multi-threshold Otsu algorithm is realized to determine the high/low threshold automatically according to the grey value statistic. Image moment method is conducted on basis of the detected moving object edges to locate the centroid and to compute the principal orientation. The experimental results based upon locating the edges of both static and moving objects proved the good robustness and the excellent accuracy of the proposed method.

scholarly journals Geometric Recognition of Moving Objects in Monocular Rotating Imagery Using Faster R-CNN

2020 ◽  
Vol 12 (12) ◽  
pp. 1908
Author(s):  
Tzu-Yi Chuang ◽  
Jen-Yu Han ◽  
Deng-Jie Jhan ◽  
Ming-Der Yang
Keyword(s):  
Object Recognition ◽  
Moving Objects ◽  
Low Cost ◽  
Three Dimensional ◽  
Real Life ◽  
Moving Object ◽  
Camera Motion ◽  
Ptz Camera ◽  
Real Life Data ◽  
Close Range

Moving object detection and tracking from image sequences has been extensively studied in a variety of fields. Nevertheless, observing geometric attributes and identifying the detected objects for further investigation of moving behavior has drawn less attention. The focus of this study is to determine moving trajectories, object heights, and object recognition using a monocular camera configuration. This paper presents a scheme to conduct moving object recognition with three-dimensional (3D) observation using faster region-based convolutional neural network (Faster R-CNN) with a stationary and rotating Pan Tilt Zoom (PTZ) camera and close-range photogrammetry. The camera motion effects are first eliminated to detect objects that contain actual movement, and a moving object recognition process is employed to recognize the object classes and to facilitate the estimation of their geometric attributes. Thus, this information can further contribute to the investigation of object moving behavior. To evaluate the effectiveness of the proposed scheme quantitatively, first, an experiment with indoor synthetic configuration is conducted, then, outdoor real-life data are used to verify the feasibility based on recall, precision, and F1 index. The experiments have shown promising results and have verified the effectiveness of the proposed method in both laboratory and real environments. The proposed approach calculates the height and speed estimates of the recognized moving objects, including pedestrians and vehicles, and shows promising results with acceptable errors and application potential through existing PTZ camera images at a very low cost.

scholarly journals Compressive Imaging of Moving Object Based on Linear Array Sensor

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Changjun Zha ◽  
Yao Li ◽  
Jinyao Gui ◽  
Huimin Duan ◽  
Tailong Xu
Keyword(s):  
Moving Objects ◽  
Linear Array ◽  
Matching Pursuit ◽  
Moving Object ◽  
Imaging Method ◽  
Compressive Imaging ◽  
Array Sensor ◽  
Object Based ◽  
Direct Separation ◽  
Matching Pursuit Algorithm

Using the characteristics of a moving object, this paper presents a compressive imaging method for moving objects based on a linear array sensor. The method uses a higher sampling frequency and a traditional algorithm to recover the image through a column-by-column process. During the compressive sampling stage, the output values of the linear array sensor are multiplied by a coefficient that is a measurement matrix element, and then the measurement value can be acquired by adding all the multiplication values together. During the reconstruction stage, the orthogonal matching pursuit algorithm is used to recover the original image when all the measurement values are obtained. Numerical simulations and experimental results show that the proposed compressive imaging method not only effectively captures the information required from the moving object for image reconstruction but also achieves direct separation of the moving object from a static scene.

A Gait Feature Extraction Algorithm Based on Fusion of Spatial and Frequency Feature

2013 ◽  
Vol 291-294 ◽  
pp. 2492-2495
Author(s):  
Xiao Ke Zhu ◽  
Xiao Pan Chen ◽  
Fan Zhang
Keyword(s):  
Feature Extraction ◽  
Moving Objects ◽  
Gait Recognition ◽  
Morphological Processing ◽  
Moving Object ◽  
Classification Rate ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm ◽  
Gait Feature ◽  
Frequency Feature

In order to enhance the accuracy of gait recognition, a new gait feature extraction algorithm is proposed. Firstly, the gait images are preprocessed to extract moving objects, including background modeling, moving object extracting and morphological processing. Secondly, an equidistant slicing curve model based on system of polar coordinate is designed to slice the moving object, and the slicing vector is used to describe the spatial feature; Thirdly, the slicing vector is converted into frequency signal by Fourier transform to extract the frequency feature. Finally, the above two features are fused and used for the classification. The experimental results show that proposed algorithm provides higher correct classification rate than the algorithms using single feature, and meets the requirements of the real-time.

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