scholarly journals Implementasi dan Uji Kinerja Algoritma Background Subtraction pada ESP32

2019 ◽  
Vol 8 (2) ◽  
pp. 59-65
Author(s):  
Didit Andri Jatmiko ◽  
Salita Ulitia Prini
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Object Detection ◽  
Background Subtraction ◽  
Background Model ◽  
Current Frame

Salah satu hal penting pada computer vision adalah ciri (feature) citra. Ciri digunakan sebagai dasar untuk mendeteksi objek, baik itu benda, manusia maupun hewan. Ciri citra yang biasa digunakan dalam penelitian antara lain tepian, sudut, bentuk maupun gradient histogram. Penelitian ini menjelaskan kinerja algoritma background subtraction pada unit pemroses berdaya rendah sebagai salah satu algoritma pada computer vision. Algoritma ini memiliki kompleksitas yang rendah dan dapat digunakan untuk mendeteksi objek sehingga berpotensi diterapkan pada kamera keamanan. Algoritma ini bekerja dengan melakukan pengurangan nilai piksel current frame dengan background model. Penelitian ini telah berhasil menerapkan algoritma dasar pengolahan citra, yaitu algoritma background subtraction pada modul ESP32. Pengujian menggunakan input citra yang memiliki dimensi 80x60 piksel dengan format warna 8bit grayscale. Ukuran frame citra 80 x 60 piksel dipilih sebagai citra uji karena keterbatasan memory DRAM EPS32 sebesar 328 KB (kilobyte). Implementasi pada modul ESP32 yang dilengkapi dengan mikroprosesor Xtensa 32-bit LX6 yang bekerja pada frekuensi 240MHz dapat memproses algoritma background subtraction 10000 kali dalam waktu ±2000ms menggunakan input citra uji tersebut. Kata Kunci – Background Subtraction; ESP32; Image Processing; Microcontroller; Object Detection.

Block Background Subtraction Method Based on ViBe

2014 ◽  
Vol 556-562 ◽  
pp. 3549-3552
Author(s):  
Lian Fen Huang ◽  
Qing Yue Chen ◽  
Jin Feng Lin ◽  
He Zhi Lin
Keyword(s):  
Spatial Correlation ◽  
Background Subtraction ◽  
Video Sequence ◽  
Moving Objects ◽  
Moving Object ◽  
Background Model ◽  
Subtraction Method ◽  
Current Frame ◽  
The Difference ◽  
Set Up

The key of background subtraction which is widely used in moving object detecting is to set up and update the background model. This paper presents a block background subtraction method based on ViBe, using the spatial correlation and time continuity of the video sequence. Set up the video sequence background model firstly. Then, update the background model through block processing. Finally employ the difference between the current frame and background model to extract moving objects.

scholarly journals Ball Classification through Object Detection using Deep Learning for Handball

Mekatronika ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 49-54
Author(s):  
Arzielah Ashiqin Alwi ◽  
Ahmad Najmuddin Ibrahim ◽  
Muhammad Nur Aiman Shapiee ◽  
Muhammad Ar Rahim Ibrahim ◽  
Mohd Azraai Mohd Razman ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Deep Learning ◽  
Performance Analysis ◽  
Object Detection ◽  
High Speed ◽  
Detection Accuracy ◽  
Narrow Angle ◽  
Target Locations ◽  
Ball Detection

Dynamic gameplay, fast-paced and fast-changing gameplay, where angle shooting (top and bottom corner) has the best chance of a good goal, are the main aspects of handball. When it comes to the narrow-angle area, the goalkeeper has trouble blocked the goal. Therefore, this research discusses image processing to investigate the shooting precision performance analysis to detect the ball's accuracy at high speed. In the handball goal, the participants had to complete 50 successful shots at each of the four target locations. Computer vision will then be implemented through a camera to identify the ball, followed by determining the accuracy of the ball position of floating, net tangle and farthest or smallest using object detection as the accuracy marker. The model will be trained using Deep Learning (DL)  models of YOLOv2, YOLOv3, and Faster R-CNN and the best precision models of ball detection accuracy were compared. It was found that the best performance of the accuracy of the classifier Faster R-CNN produces 99% for all ball positions.

Improved Background Subtraction Technique for detecting Moving Object

2020 ◽  
Vol 13 ◽  
Author(s):  
Tannistha Pal
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Image Enhancement ◽  
Background Subtraction ◽  
Moving Object ◽  
Detection Methods ◽  
Subtraction Method ◽  
Subtraction Technique ◽  
Visual Background ◽  
Computer Vision Applications

Introduction: Moving object detection from videos is among the most difficult task in different areas of computer vision applications. Among the traditional object detection methods, researchers conclude that Background Subtraction method carried out better in aspects of execution time and output quality. Mehtod: Visual background extractor is a renowned algorithm in Background Subtraction method for detecting moving object in various applications. In the recent years, lots of work has been carried out to improve the existing Visual Background extractor algorithm. Result: After investigating many state of art techniques and finding out the research gaps, this paper presents an improved background subtraction technique based on morphological operation and 2D median filter for detecting moving object which reduces the noise in the output video and also enhances its accuracy at a very limited additional cost. Experimental results in several benchmark datasets confirmed the superiority of the proposed method over the state-of-the-art object detection methods. Conclusion: In this article, a method has been proposed for moving object detection where the quality of the output object is enhanced and good accuracy is achieved. This method provide with accurate experimental results, which helps in efficient object detection. The proposed technique also deals with Visual Background extractor Algorithm along with the Image Enhancement Procedure like Morphological and 2-D Filtering at a limited additional cost Discussion: This article worked on certain specific field, like noise reduction and image enhancement of output images of the existing ViBe Algorithm. The technique proposed in this article will be beneficial for various computer vision applications like video surveillance, road condition monitoring, airport safety, human activity analysis, monitoring marine border for security purpose etc.

Detecting and Tracking Segmentation of Moving Objects Using Graph Cut Algorithm

2018 ◽  
pp. 177-192
Author(s):  
Raviraj Pandian ◽  
Ramya A.
Keyword(s):  
Object Detection ◽  
Moving Objects ◽  
Graph Cut ◽  
Background Model ◽  
Camera Motion ◽  
Current Frame ◽  
Outdoor Environments ◽  
Previous Frame ◽  
Indoor And Outdoor Environments ◽  
Graph Cut Segmentation

Real-time moving object detection, classification, and tracking capabilities are presented with system operates on both color and gray-scale video imagery from a stationary camera. It can handle object detection in indoor and outdoor environments and under changing illumination conditions. Object detection in a video is usually performed by object detectors or background subtraction techniques. The proposed method determines the threshold automatically and dynamically depending on the intensities of the pixels in the current frame. In this method, it updates the background model with learning rate depending on the differences of the pixels in the background model of the previous frame. The graph cut segmentation-based region merging algorithm approaches achieve both segmentation and optical flow computation accurately and they can work in the presence of large camera motion. The algorithm makes use of the shape of the detected objects and temporal tracking results to successfully categorize objects into pre-defined classes like human, human group, and vehicle.

scholarly journals Hand Detection for Safety to Avoid Accidents

2021 ◽  
Vol 9 (VI) ◽  
pp. 3547-3552
Author(s):  
Eega Krishna Chaithanya
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Object Detection ◽  
Time Data ◽  
Image Processing Algorithm ◽  
Hand Detection ◽  
Safety Level ◽  
Real Time Data ◽  
Imaginary Line ◽  
Stop Line

Detecting the hand when it crosses the safety level and in return it also raises an alert in the form of alarm. So that the threat can be identified and proper measures are taken to overcome that. The methodology of the project goes as follows, taking input from camera , Image processing to detect hand, Projecting a line using computer vision, Raising alarm when hand crosses this projected safety line. The real time data is taken from the camera as an input to the Image processing algorithm. Then this input is processed to find the hand in image in it and checks whether the hand is crossing that safety line. If that hand is crossing the safety line we can simply raise alarm. The applications of the project are to the Employees who are working at industry are pushing the material into shredder machine. But somehow while pushing these material into shredder machine the employees are pushing their hands itself in the flow of work and the hands of employees were cut in that cause. So from a certain distance from shredder machine input we project a imaginary line using computer vision, So that if any hand crossing that imaginary line which is for safety we will raise an alarm. In addition, we can also extend the applications, by just replacing hand with the Bike, we can detect the bike, which is crossing the staggered stop line, and we can punish or fine them. As a part of object detection we are using Single short multibox detector.

Role of Clustering Techniques in Effective Image Segmentation

2018 ◽  
pp. 128-160
Author(s):  
Bhavneet Kaur ◽  
Meenakshi Sharma
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Segmentation ◽  
Object Detection ◽  
Real Time ◽  
Vision Research ◽  
Medical Equipment ◽  
Additional Advantage ◽  
Clustering Techniques

Image segmentation is gauged as an essential stage of representation in image processing. This process segregates a digitized image into various categorized sections. An additional advantage of distinguishing dissimilar objects can be represented within this state of the art. Numerous image segmentation techniques have been proposed by various researchers, which maintained a smooth and easy timely evaluation. In this chapter, an introduction to image processing along with segmentation techniques, computer vision fundamentals, and its applied applications that will be of worth to the image processing and computer vision research communities has been deeply studied. It aims to interpret the role of various clustering-based image segmentation techniques specifically. Use of the proposed chapter if made in real time can project better outcomes in object detection and recognition, which can then later be applied in numerous applications and devices like in robots, automation, medical equipment, etc. for safety, advancement, and betterment of society.

scholarly journals An Analysis on Object Recognition Using Convolutional Neural Networks

2021 ◽  
Vol 10 (3) ◽  
pp. 1928-1935
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Computer Vision ◽  
Object Recognition ◽  
Object Detection ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Critical Analysis ◽  
Data Volume ◽  
Fundamental Reason

The global development and progress in scientific paraphernalia and technology is the fundamental reason for the rapid increasein the data volume. Several significant techniques have been introducedfor image processing and object detection owing to this advancement. The promising features and transfer learning of ConvolutionalNeural Network (CNN) havegained much attention around the globe by researchers as well as computer vision society, as a result of which, several remarkable breakthroughs were achieved. This paper comprehensively reviews the data classification, history as well as architecture of CNN and well-known techniques bytheir boons and absurdities. Finally, a discussion for implementation of CNN over object detection for effectual results based on their critical analysis and performances is presented

scholarly journals Method of Moving Region Detection for Static Camera

2018 ◽  
Vol 12 (6) ◽  
pp. 3626-3633
Author(s):  
Pravesh Kumar Goel ◽  
Paresh P. Kotak ◽  
Amit Gupta
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Background Subtraction ◽  
Video Sequence ◽  
Moving Objects ◽  
Moving Object Detection ◽  
Region Detection ◽  
Computer Vision Applications ◽  
Object Center ◽  
Background Image

The moving object detection from a stationary video sequence is a primary task in various computer vision applications. In this proposed system; three processing levels are suppose to perform: detects moving objects region from the background image; reduce noise from the pixels of detected region and extract meaningful objects and their features (area of object, center point of area etc.). In this paper; background subtraction techniques is used for segments moving objects from the background image, which is capable for pixel level processing. Morphology operation (Erosion and dilation) are used to remove pixel to pixel noise. In last level, CCL algorithm is used for sorts out foregrounds pixels are grouped into meaningful connected regions and their features.

scholarly journals Analysis of Object detection Method using Open CV – Python

2020 ◽  
pp. 422-426
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Object Recognition ◽  
Object Detection ◽  
Computer Technology ◽  
Character Recognition ◽  
Detection Method ◽  
Processing Data ◽  
Basic Concepts ◽  
Detection And Recognition

Object detection is a very well-known computer technology connected with computer vision, image processing, data collection and character recognition that focuses on detection of any object or its instances of a certain class (such as humans, flowers, animals, number plates, vehicles etc.) presented in the form of digital images and videos. There are various applications on object detection that have been well researched including face detection & recognition, character recognition and prediction, and number plate detection. Object detection and recognition are used in very vast cases and scenarios including retrieval, surveillance, detection of over speeding of vehicles and a lot more cases . In this research, various basic concepts used in object recognition and detection while making use of OpenCV library of python 3.8, increasing and improvising the efficiency & accuracy of object recognition and detection are presented

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