scholarly journals Development of real-time indoor human tracking system using LoRa technology

2022 ◽  
Vol 12 (1) ◽  
pp. 845
Author(s):  
Ida Syafiza Binti Md Isa ◽  
Anis Hanani
Keyword(s):  
Real Time ◽  
Tracking System ◽  
Low Cost ◽  
Power Level ◽  
Human Tracking ◽  
High Coverage ◽  
Industrial Growth ◽  
Received Signal Strength Indicator ◽  
Positioning Method ◽  
Testbed Implementation

<p>Industrial growth has increased the number of jobs hence increase the number of employees. Therefore, it is impossible to track the location of all employees in the same building at the same time as they are placed in a different department. In this work, a real-time indoor human tracking system is developed to determine the location of employees in a real-time implementation. In this work, the long-range (LoRa) technology is used as the communication medium to establish the communication between the tracker and the gateway in the developed system due to its low power with high coverage range besides requires low cost for deployment. The received signal strength indicator (RSSI) based positioning method is used to measure the power level at the receiver which is the gateway to determine the location of the employees. Different scenarios have been considered to evaluate the performance of the developed system in terms of precision and reliability. This includes the size of the area, the number of obstacles in the considered area, and the height of the tracker and the gateway. A real-time testbed implementation has been conducted to evaluate the performance of the developed system and the results show that the system has high precision and are reliable for all considered scenarios.</p>

scholarly journals Indoor Positioning Method Based on Infrared Vision and UWB Fusion

2021 ◽  
Vol 2078 (1) ◽  
pp. 012070
Author(s):  
Qianrong Zhang ◽  
Yi Li
Keyword(s):  
Real Time ◽  
Indoor Localization ◽  
Low Cost ◽  
Ultra Wideband ◽  
Coverage Area ◽  
Positioning Accuracy ◽  
Mobile Vehicle ◽  
Positioning Method ◽  
Positioning Algorithm ◽  
Infrared Vision

Abstract Ultra-wideband (UWB) has broad application prospects in the field of indoor localization. In order to make up for the shortcomings of ultra-wideband that is easily affected by the environment, a positioning method based on the fusion of infrared vision and ultra-wideband is proposed. Infrared vision assists locating by identifying artificial landmarks attached to the ceiling. UWB uses an adaptive weight positioning algorithm to improve the positioning accuracy of the edge of the UWB positioning coverage area. Extended Kalman filter (EKF) is used to fuse the real-time location information of the two. Finally, the intelligent mobile vehicle-mounted platform is used to collect infrared images and UWB ranging information in the indoor environment to verify the fusion method. Experimental results show that the fusion positioning method is better than any positioning method, has the advantages of low cost, real-time performance, and robustness, and can achieve centimeter-level positioning accuracy.

A low-cost real-time face tracking system for ITSs and SDASs

2016 ◽  
Vol 47 (8) ◽  
pp. 1111-1126
Author(s):  
Leyuan Liu ◽  
Jingying Chen ◽  
Changxin Gao ◽  
Nong Sang
Keyword(s):  
Real Time ◽  
Tracking System ◽  
Low Cost ◽  
Face Tracking

Real-time human tracking system with multiple omnidirectional vision sensors

2004 ◽  
Vol 35 (2) ◽  
pp. 79-90 ◽  
Author(s):  
Takushi Sogo ◽  
Hiroshi Ishiguro ◽  
Mohan M. Trivedi
Keyword(s):  
Real Time ◽  
Tracking System ◽  
Human Tracking ◽  
Omnidirectional Vision ◽  
Vision Sensors

Using Cumulative Histogram Maps in an Adaptive Color-Based Particle Filter for Real-Time Object Tracking

2010 ◽  
Vol 121-122 ◽  
pp. 585-590 ◽  
Author(s):  
San Lung Zhao ◽  
Shen Zheng Wang ◽  
Hsi Jian Lee ◽  
Hung I Pai
Keyword(s):  
Particle Filter ◽  
Real Time ◽  
Tracking System ◽  
Target Object ◽  
Color Histogram ◽  
Human Tracking ◽  
Speed Up ◽  
Non Gaussian ◽  
The Cost ◽  
Gaussian Estimation

The study presents a human tracking system. To tracking a person, we adopt a particle filter as tracking kernel, since the method has proven successful for tracking in non-linear and non-Gaussian estimation. In a particle filter, a set of weighted particles represents the possible target sates. In this study, we measure the weight according to both the appearances of the target object and background scene to improve the discriminability between them. In our tracker, the appearances are modeled as color histogram, since it is scale and rotation invariant. However, the color histogram extraction for a large number of overlap regions is repeated redundantly and inefficiently. To speed up it, we reduce the cost for calculating overlapped regions by creating a cumulative histogram map for the processing image. The experimental results show that the tracker has the best precision improvement, and the tracking speed is 49.7 fps for 384 × 288 resolution, when we use 600 particles. The results show that the proposed method can be applied to a real-time human tracking system with high precision.

Development and validation of a low-cost mobile robotics testbed

2012 ◽  
Vol 2 (1) ◽  
Author(s):  
Michael Johnson ◽  
Martin Hayes
Keyword(s):  
Real Time ◽  
Mobile Robotics ◽  
Vision System ◽  
Tracking System ◽  
Low Cost ◽  
Research Work ◽  
Cost Effective ◽  
Video Tracking ◽  
Research And Education ◽  
Development And Validation

AbstractThis paper considers the design, construction and validation of a low-cost experimental robotic testbed, which allows for the localisation and tracking of multiple robotic agents in real time. The testbed system is suitable for research and education in a range of different mobile robotic applications, for validating theoretical as well as practical research work in the field of digital control, mobile robotics, graphical programming and video tracking systems. It provides a reconfigurable floor space for mobile robotic agents to operate within, while tracking the position of multiple agents in real-time using the overhead vision system. The overall system provides a highly cost-effective solution to the topical problem of providing students with practical robotics experience within severe budget constraints. Several problems encountered in the design and development of the mobile robotic testbed and associated tracking system, such as radial lens distortion and the selection of robot identifier templates are clearly addressed. The testbed performance is quantified and several experiments involving LEGO Mindstorm NXT and Merlin System MiaBot robots are discussed.

scholarly journals Smart Bluetooth beacon based on LoRa Positioning method and system research

2021 ◽  
Vol 2079 (1) ◽  
pp. 012032
Author(s):  
Rui Su ◽  
Yawen Dai
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Real Life ◽  
Signal Attenuation ◽  
Positioning Accuracy ◽  
Positioning Method ◽  
New Type ◽  
Positioning Analysis ◽  
Transmission Speed ◽  
Indoor And Outdoor

Abstract In the era of the Internet of Everything, applications based on real-time location continue to appear in various industries. The indoor and outdoor positioning, analysis and management of personnel and objects can effectively improve the efficiency of production and management, which is of great significance to many industries. The use of Bluetooth beacon positioning has the advantages of low energy consumption, low cost, and fast data transmission speed. However, in real life, there are two obstacles to receiving signals, which are easily affected by the environment and the need for frequent on-site maintenance. This paper designs and implements a new type of LoRa-based smart Bluetooth beacon, which can be quickly connected to LoRa. Online monitoring and remote control can achieve better adaptation to the environment, and can fit a better signal attenuation model in the deployment environment in real time. Traditional signal strength positioning solutions have their own limitations. In order to improve the positioning accuracy of the Bluetooth beacon and the universality of the algorithm, in view of the low deployment density of beacons, the positioning accuracy is not good and the anchor circle has various situations, an optimized weighted centroid positioning scheme integrating greedy strategy is proposed.

scholarly journals Building an advanced invariant real-time human tracking system

2007 ◽  
Vol 4 (1) ◽  
pp. 57-75
Author(s):  
Fayez Idris ◽  
Zaher Abu ◽  
Rashad Rasras ◽  
Emary El
Keyword(s):  
Real Time ◽  
Pattern Matching ◽  
Personal Computer ◽  
Tracking System ◽  
Image Sequences ◽  
Tracking Algorithm ◽  
Human Tracking ◽  
Absolute Difference ◽  
To Come

Real-time human tracking is very important in surveillance and robot applications. We note that the performance of any human tracking system depends on its accuracy and its ability to deal with various human sizes in a fast way. In this paper, we combined the presented works in [1, 2] to come with new human tracking algorithm that is robust to background and lighting changes and does not require special hardware components. In addition this system can handle various scales of human images. The proposed system uses sum of absolute difference (SAD) with thresholding as has been described in [2] and compares the output with the predefined person pattern using the technique which has been described in [1]. Using the combination between [1,2] approaches will enhance the performance and speed of the tracking system since pattern matching has been performed according to just one pattern. After matching stage, a specific file is created for each tracked person, this file includes image sequences for that person. The proposed system handles shadows removal, lighting changes, and background changes with infinite pattern scales using standard personal computer.

scholarly journals Low-cost intelligent surveillance system based on fast CNN

2021 ◽  
Vol 7 ◽  
pp. e402
Author(s):  
Zaid Saeb Sabri ◽  
Zhiyong Li
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Tracking System ◽  
Low Cost ◽  
Saliency Detection ◽  
Detection Algorithm ◽  
Raspberry Pi ◽  
Surveillance Systems ◽  
Single Board Computer ◽  
Computational Systems

Smart surveillance systems are used to monitor specific areas, such as homes, buildings, and borders, and these systems can effectively detect any threats. In this work, we investigate the design of low-cost multiunit surveillance systems that can control numerous surveillance cameras to track multiple objects (i.e., people, cars, and guns) and promptly detect human activity in real time using low computational systems, such as compact or single board computers. Deep learning techniques are employed to detect certain objects to surveil homes/buildings and recognize suspicious and vital events to ensure that the system can alarm officers of relevant events, such as stranger intrusions, the presence of guns, suspicious movements, and identified fugitives. The proposed model is tested on two computational systems, specifically, a single board computer (Raspberry Pi) with the Raspbian OS and a compact computer (Intel NUC) with the Windows OS. In both systems, we employ components, such as a camera to stream real-time video and an ultrasonic sensor to alarm personnel of threats when movement is detected in restricted areas or near walls. The system program is coded in Python, and a convolutional neural network (CNN) is used to perform recognition. The program is optimized by using a foreground object detection algorithm to improve recognition in terms of both accuracy and speed. The saliency algorithm is used to slice certain required objects from scenes, such as humans, cars, and airplanes. In this regard, two saliency algorithms, based on local and global patch saliency detection are considered. We develop a system that combines two saliency approaches and recognizes the features extracted using these saliency techniques with a conventional neural network. The field results demonstrate a significant improvement in detection, ranging between 34% and 99.9% for different situations. The low percentage is related to the presence of unclear objects or activities that are different from those involving humans. However, even in the case of low accuracy, recognition and threat identification are performed with an accuracy of 100% in approximately 0.7 s, even when using computer systems with relatively weak hardware specifications, such as a single board computer (Raspberry Pi). These results prove that the proposed system can be practically used to design a low-cost and intelligent security and tracking system.

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