scholarly journals A Moving Object Detection Method Using Deep Learning-Based Wireless Sensor Networks

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Linghua Zhao ◽  
Zhihua Huang
Keyword(s):  
Wireless Sensor Networks ◽  
Deep Learning ◽  
Sensor Networks ◽  
Object Detection ◽  
Moving Objects ◽  
Detection Method ◽  
Learning Algorithm ◽  
Wireless Sensor ◽  
Moving Target ◽  
Deep Learning Algorithm

Aiming at the problem of real-time detection and location of moving objects, the deep learning algorithm is used to detect moving objects in complex situations. In this paper, based on the deep learning algorithm of wireless sensor networks, a novel target motion detection method is proposed. This method uses the deep learning model to extract visual potential representation features through offline similarity function ranking learning and online model incremental update and uses the hierarchical clustering algorithm to achieve target detection and positioning; the low-precision histogram and high-precision histogram cascade the method which determines the correct position of the target and achieves the purpose of detecting the moving target. In order to verify the advantages and disadvantages of the deep learning algorithm compared with traditional moving object detection methods, a large number of comparative experiments are carried out, and the experimental results were analyzed qualitatively and quantitatively from a statistical perspective. The results show that, compared with the traditional methods, the deep learning algorithm based on the wireless sensor network proposed in this paper is more efficient. The detection and positioning method do not produce the error accumulation phenomenon and has significant advantages and robustness. The moving target can be accurately detected with a small computational cost.

scholarly journals K-Clustering for the Recognition of Moving Entity by Wireless Sensor Networks Using Deep Learning

2021 ◽  
Author(s):  
Thiyagarajan R ◽  
Balajivijayan V ◽  
Rajalakshmi D
Keyword(s):  
Wireless Sensor Networks ◽  
Deep Learning ◽  
Sensor Networks ◽  
Learning Algorithm ◽  
Clustering Algorithms ◽  
Computational Cost ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Deep Learning Algorithm ◽  
Positioning Method

Abstract For the detection of the moving entity, a deep learning algorithm is used. To detect the complex type of situations, real-time monitoring of a moving object has to be detected. Using the deep learning method, wireless sensor networks are diagnosed using virtual representation features. Using the k-clustering technique, it achieves the target detection and can determine the positioning. It can determine the characteristics based on the precision. The wireless sensor networks are proposed and analyzed by means of a statistical approach. Statistical way of clustering up of the data detects the precision rate and its positioning state. The positioning method eventually reduces the error which accumulates the efficiency. The main advantages are the robustness and efficiency to improve the performance. These moving targets can be detected with less computational cost. In the field of pervasive computing, the recognition of a moving target can be improvised. The sensor node transmits the information, i.e., communication to nodes. In this paper, the researches focus on wireless sensor data along with a moving entity. This research is mainly used in healthcare and AI-based applications. By using the IoT with wireless sensor networks, the detection of a moving entity can be determined by using the combination of k-clustering algorithms. This deep learning algorithm reduces the time complexity and determines integrity in data.

scholarly journals Object Detection Method by using Yolov3 with Machine Learning

2020 ◽  
Vol 8 (6) ◽  
pp. 5247-5250
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Object Detection ◽  
Moving Objects ◽  
Learning Algorithm ◽  
Detection Algorithm ◽  
Video Object ◽  
Long Distance ◽  
Deep Learning Algorithm ◽  
Tracking Movement

Detecting the object is a vision technique of a computer for detectng or locating long distance or short distance objects and images. Object detection algorithm mainly works on the machine learning and the artificial intelligence algorithms. Present trending algorithm in detecting the object is deep learning algorithm, By using the deep learning algorithm we can get the accurate results of the object which is detected. it is mainly or widely used in the system vision tasks like video object co-segmentations, tracking movement of the ball in the ground, image annotating etc. Each and every object has its own features ,for example if you select the ball , Actually all the ball are in the round shape but in every game different type of balls are used ,object detection camera will detect the ball it will check the ball specifications with its data if any data was matched with its data base the system will display all the specifications of ball. By using the deep learning algorithm we introduced one new technique to detect detect object accurately the algorithm is named as the YOLO V3 we can detect the very small objects and the fastly moving objects easily. This yolo v3 will convert the image into N number of layers and it will work on the each and every minute spot on the image.

scholarly journals Moving Objects Detection & Recognition using Hybrid Canny Edge Detection Algorithm in Digital Image Processing

2019 ◽  
Vol 8 (9S3) ◽  
pp. 56-60
Keyword(s):  
Object Detection ◽  
Moving Objects ◽  
Detection Method ◽  
Learning Algorithm ◽  
Detection Algorithm ◽  
Moving Objects Detection ◽  
Canny Edge ◽  
Objects Detection ◽  
Canny Edge Detection Algorithm ◽  
Day By Day

Recognition and detection of an object in the watched scenes is a characteristic organic capacity. Animals and human being play out this easily in day by day life to move without crashes, to discover sustenance, dodge dangers, etc. Be that as it may, comparable PC techniques and calculations for scene examination are not all that direct, in spite of their exceptional advancement. Object detection is the process in which finding or recognizing cases of articles (for instance faces, mutts or structures) in computerized pictures or recordings. This is the fundamental task in computer. For detecting the instance of an object and to pictures having a place with an article classification object detection method usually used learning algorithm and extracted features. This paper proposed a method for moving object detection and vehicle detection.

scholarly journals A Lightweight Intrusion Detection Method Based on Fuzzy Clustering Algorithm for Wireless Sensor Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Hongchun Qu ◽  
Libiao Lei ◽  
Xiaoming Tang ◽  
Ping Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Intrusion Detection ◽  
Sensor Network ◽  
Clustering Algorithm ◽  
Detection Method ◽  
Base Station ◽  
Simulation Software ◽  
Wireless Sensor ◽  
Sensor Monitoring

For resource-constrained wireless sensor networks (WSNs), designing a lightweight intrusion detection technology has been a hot and difficult issue. In this paper, we proposed a lightweight intrusion detection method that was able to directly map the network status into sensor monitoring data received by base station, so that base station can sense the abnormal changes in the network. Our method is highlighted by the fusion of fuzzy c-means algorithm, one-class SVM, and sliding window procedure to effectively differentiate network attacks from abnormal data. Finally, the proposed method was tested on the wireless sensor network simulation software EXata and in real applications. The results showed that the intrusion detection method in this paper could effectively identify whether the abnormal data came from a network attack or just a noise. In addition, extra energy consumption can be avoided in all sensor monitoring nodes of the sensor network where our method has been deployed.

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