scholarly journals Development of an Automatic Tracking Camera System Integrating Image Processing and Machine Learning

2021 ◽  
Vol 33 (6) ◽  
pp. 1303-1314
Author(s):  
Masato Fujitake ◽  
Makito Inoue ◽  
Takashi Yoshimi ◽  
◽  
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Object Detection ◽  
Object Tracking ◽  
Tracking System ◽  
Construction Technology ◽  
Camera System ◽  
Automatic Tracking ◽  
Machine Operators ◽  
Surveillance Cameras

This paper describes the development of a robust object tracking system that combines detection methods based on image processing and machine learning for automatic construction machine tracking cameras at unmanned construction sites. In recent years, unmanned construction technology has been developed to prevent secondary disasters from harming workers in hazardous areas. There are surveillance cameras on disaster sites that monitor the environment and movements of construction machines. By watching footage from the surveillance cameras, machine operators can control the construction machines from a safe remote site. However, to control surveillance cameras to follow the target machines, camera operators are also required to work next to machine operators. To improve efficiency, an automatic tracking camera system for construction machines is required. We propose a robust and scalable object tracking system and robust object detection algorithm, and present an accurate and robust tracking system for construction machines by integrating these two methods. Our proposed image-processing algorithm is able to continue tracking for a longer period than previous methods, and the proposed object detection method using machine learning detects machines robustly by focusing on their component parts of the target objects. Evaluations in real-world field scenarios demonstrate that our methods are more accurate and robust than existing off-the-shelf object tracking algorithms while maintaining practical real-time processing performance.

scholarly journals Real-time object tracking system based on field-programmable gate array and convolution neural network

2016 ◽  
Vol 14 (1) ◽  
pp. 172988141668270 ◽  
Author(s):  
Congyi Lyu ◽  
Haoyao Chen ◽  
Xin Jiang ◽  
Peng Li ◽  
Yunhui Liu
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Object Tracking ◽  
Real Time ◽  
Field Programmable Gate Array ◽  
Tracking System ◽  
Convolution Neural Network ◽  
Processing Unit ◽  
Field Programmable ◽  
Gate Array

Vision-based object tracking has lots of applications in robotics, like surveillance, navigation, motion capturing, and so on. However, the existing object tracking systems still suffer from the challenging problem of high computation consumption in the image processing algorithms. The problem can prevent current systems from being used in many robotic applications which have limitations of payload and power, for example, micro air vehicles. In these applications, the central processing unit- or graphics processing unit-based computers are not good choices due to the high weight and power consumption. To address the problem, this article proposed a real-time object tracking system based on field-programmable gate array, convolution neural network, and visual servo technology. The time-consuming image processing algorithms, such as distortion correction, color space convertor, and Sobel edge, Harris corner features detector, and convolution neural network were redesigned using the programmable gates in field-programmable gate array. Based on the field-programmable gate array-based image processing, an image-based visual servo controller was designed to drive a two degree of freedom manipulator to track the target in real time. Finally, experiments on the proposed system were performed to illustrate the effectiveness of the real-time object tracking system.

scholarly journals Fast and Economical Object Tracking using Raspberry Pi 3.0

2019 ◽  
Vol 8 (6S) ◽  
pp. 336-338
Keyword(s):  
Computer Vision ◽  
Power Consumption ◽  
Low Power ◽  
Object Detection ◽  
Object Tracking ◽  
Tracking System ◽  
Low Power Consumption ◽  
Raspberry Pi ◽  
Multiple Object Detection ◽  
Vision Application

This paper proposes a way to construct a financially cheap and fast object tracking using Raspberry Pi3. Multiple object detection is an important step in any computer vision application. Since the number of cameras included is more these gadgets are compelled by expense per hub, control utilization and handling power. We propose a tracking system with low power consumption. The framework is completely designed with python and OpenCV. The tracking quality and accuracy is measured using publicly available datasets.

scholarly journals Deteksi Objek Berwarna Merah Secara Real Time Dengan Algoritma Color Filtering

2019 ◽  
Vol 3 (2) ◽  
pp. 140
Author(s):  
Yona Fransiska Dewi ◽  
Nurul Fadillah
Keyword(s):  
Image Processing ◽  
Research And Development ◽  
Object Detection ◽  
Object Tracking ◽  
Digital Image Processing ◽  
Digital Image ◽  
Image Processing Techniques ◽  
Detection And Tracking ◽  
Web Camera ◽  
Processing Techniques

The various knowledge and techniques of digital image processing currently available vary greatly. Research and development has been carried out towards object detection and tracking. Color is one of the parameters used to detect and track objects. Humans can distinguish a color, but a computer may not necessarily recognize that color. Digital image processing techniques that can recognize colors, one of which is color filtering. In this study, Color filtering is a technique of processing digital images based on specific colors, detecting and tracking colors by using a web camera (webcam) and red objects. Object Tracking is the process of following an object that moves and moves position, so that the colored object being tracked will draw in realtime with the results of the colors that can be selected.

scholarly journals Object Detection and Identification in Surveillance Images using Image Processing

2019 ◽  
Vol 9 (1S3) ◽  
pp. 300-305
Keyword(s):  
Image Processing ◽  
Object Detection ◽  
Object Tracking ◽  
Range Size ◽  
Its Sequence ◽  
Surveillance Camera ◽  
Color Range ◽  
Detection And Identification ◽  
Illumination Changes ◽  
Tracking And Recognition

The goal of object detection and identification in surveillance images using image processing is to detect a particular part of the image from surveillance camera like an object’s position, movement, and its sequence. Object tracking and recognition deal with recognizing the image of video which can differ in color, range, size, illumination changes with time and some cluttered images. As this paper has been surveying and an algorithm has been proposed and implemented, the identified object has freed from the shadow, clutter, illumination changes were detected and eliminated appropriately.

scholarly journals Electronic Eye for Blind People with Object Detection and Audio Output Using Image Processing

2021 ◽  
pp. 403-406
Author(s):  
Atharva Shewale ◽  
Mrunalini Mahakalkar ◽  
Vijay Pawar ◽  
Yajan Bharad ◽  
Dr. Shwetambari Chiwhane
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Object Detection ◽  
Real Time ◽  
Blind People ◽  
Blind Person ◽  
Great Problem ◽  
Unknown Environment ◽  
Audio Output ◽  
Electronic Eye

One of the major issues faced by Blind people is detecting and recognizing an object. The objective of this project is to help the blind people because mobility of blind people is always a great problem. The mobility of blind people in unknown environment seems impossible without external help, because they don’t have any proper idea about their surroundings. So, we are developing a electronic eye which helps them to know about their surroundings and also guide them during travelling. Developing a system based on image processing using DNN algorithm which is able to labeling objects with the help of OpenCV and Tensor flow libraries and converting the labeled text in to speech and producing output in the form of audio to make the blind person aware of the object in front of him or her. The scope of this system is also measuring the distance of the object from the person and reporting the same Object detection using image processing and Machine Learning. It searches the object. We want to innovate our system the possibility of using the hearing sense to understand real time objects. For the security purpose track blind people in real time environment.

Employing of Object Tracking System in Public Surveillance Cameras to Enforce Quarantine and Social Distancing Using Parallel Machine Learning Techniques

2021 ◽  
Vol 13 (3) ◽  
pp. 171-180
Author(s):  
Sokyna Alqatawneh ◽  
Khalid Jaber ◽  
Mosa Salah ◽  
Dalal Yehia ◽  
Omayma Alqatawneh ◽  
...  
Keyword(s):  
Object Tracking ◽  
Moving Objects ◽  
Residential Buildings ◽  
Tracking System ◽  
Machine Learning Techniques ◽  
Time Base ◽  
Social Distancing ◽  
Public Surveillance ◽  
Surveillance Cameras ◽  
Mentoring System

Like many countries, Jordan has resorted to lockdown in an attempt to contain the outbreak of Coronavirus (Covid-19). A set of precautions such as quarantines, isolations, and social distancing were taken in order to tackle its rapid spread of Covid-19. However, the authorities were facing a serious issue with enforcing quarantine instructions and social distancing among its people. In this paper, a social distancing mentoring system has been designed to alert the authorities if any of the citizens violated the quarantine instructions and to detect the crowds and measure their social distancing using an object tracking technique that works in real-time base. This system utilises the widespread surveillance cameras that already exist in public places and outside many residential buildings. To ensure the effectiveness of this approach, the system uses cameras deployed on the campus of Al-Zaytoonah University of Jordan. The results showed the efficiency of this system in tracking people and determining the distances between them in accordance with public safety instructions. This work is the first approach to handle the classification challenges for moving objects using a shared-memory model of multicore techniques. Keywords: Covid-19, Parallel computing, Risk management, Social distancing, Tracking system.

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