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.

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.

Multiple Object Tracking by Scale Space Representation of Objects, Method of Linear Assignment, and Kalman Filter

2016 ◽  
pp. 236-259
Author(s):  
Kumar S. Ray ◽  
Soma Ghosh ◽  
Kingshuk Chatterjee ◽  
Debayan Ganguly
Keyword(s):  
Kalman Filter ◽  
Object Tracking ◽  
Moving Objects ◽  
Tracking System ◽  
Scale Space ◽  
Space Representation ◽  
Occluded Objects ◽  
Video Scene ◽  
Cost Parameters ◽  
Linear Assignment

This chapter presents a multi-object tracking system using scale space representation of objects, the method of linear assignment and Kalman filter. In this chapter basically two very prominent problems of multi object tracking have been resolved; the two prominent problems are (i) irrespective of the size of the objects, tracking all the moving objects simultaneously and (ii) tracking of objects under partial and/or complete occlusion. The primary task of tracking multiple objects is performed by the method of linear assignment for which few cost parameters are computed depending upon the extracted features of moving objects in video scene. In the feature extraction phase scale space representation of objects have been used. Tracking of occluded objects is performed by Kalman filter.

scholarly journals Moving Camera-Based Object Tracking Using Adaptive Ground Plane Estimation and Constrained Multiple Kernels

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Tao Liu ◽  
Yong Liu
Keyword(s):  
Object Tracking ◽  
Moving Objects ◽  
Intelligent Transportation System ◽  
Tracking System ◽  
Ground Plane ◽  
Estimation Algorithm ◽  
Moving Camera ◽  
Moving Vehicle ◽  
Multiple Kernel ◽  
Yaw Angle

Moving camera-based object tracking method for the intelligent transportation system (ITS) has drawn increasing attention. The unpredictability of driving environments and noise from the camera calibration, however, make conventional ground plane estimation unreliable and adversely affecting the tracking result. In this paper, we propose an object tracking system using an adaptive ground plane estimation algorithm, facilitated with constrained multiple kernel (CMK) tracking and Kalman filtering, to continuously update the location of moving objects. The proposed algorithm takes advantage of the structure from motion (SfM) to estimate the pose of moving camera, and then the estimated camera’s yaw angle is used as a feedback to improve the accuracy of the ground plane estimation. To further robustly and efficiently tracking objects under occlusion, the constrained multiple kernel tracking technique is adopted in the proposed system to track moving objects in 3D space (depth). The proposed system is evaluated on several challenging datasets, and the experimental results show the favorable performance, which not only can efficiently track on-road objects in a dashcam equipped on a free-moving vehicle but also can well handle occlusion in the tracking.

scholarly journals Object Tracking System using Iot

2020 ◽  
Vol 9 (3) ◽  
pp. 1357-1362
Keyword(s):  
Object Tracking ◽  
Moving Objects ◽  
Daily Life ◽  
Tracking System ◽  
Transport Service ◽  
Service Companies ◽  
Communication Devices

Generally every person might have the habit of forgetting their things where they kept or losing their things like vehicles, car keys, house keys or some valuable things. If our mobile is lost we can track it using our Google account but we cannot track any other object in the similar way. This problem is very common when we loose our things we may face difficulty in finding them. Instead of finding them we replace them with new ones sometimes our data will be lost which we cannot retrieve back. So in our project we can track our lost objects by implementing IoT. We can implement this using Bluetooth but there are few limitations which can be resolved using IoT. The different components generally used are GPS we can get the location of the object that we want to track but if the signal is transmitted using GPS our communication devices cannot receive them directly. So we use GSM module which will transfer those signals to the communication devices. The main purpose of our project is it gives the location of the object and traces it. This will help us to track any stable object or moving objects like vehicles, keys, trucks etc. This device will send the location co-ordinates to the user or person who is tracking the object. This device can use by the transport service companies and every person in their daily life. It also keeps on updating the travel status of the object as the object moves on and processes the queries of the owner in tracking the object.

Multiple Object Tracking by Scale Space Representation of Objects, Method of Linear Assignment, and Kalman Filter

2018 ◽  
pp. 800-823
Author(s):  
Kumar S. Ray ◽  
Soma Ghosh ◽  
Kingshuk Chatterjee ◽  
Debayan Ganguly
Keyword(s):  
Kalman Filter ◽  
Object Tracking ◽  
Moving Objects ◽  
Tracking System ◽  
Scale Space ◽  
Space Representation ◽  
Occluded Objects ◽  
Video Scene ◽  
Cost Parameters ◽  
Linear Assignment

This chapter presents a multi-object tracking system using scale space representation of objects, the method of linear assignment and Kalman filter. In this chapter basically two very prominent problems of multi object tracking have been resolved; the two prominent problems are (i) irrespective of the size of the objects, tracking all the moving objects simultaneously and (ii) tracking of objects under partial and/or complete occlusion. The primary task of tracking multiple objects is performed by the method of linear assignment for which few cost parameters are computed depending upon the extracted features of moving objects in video scene. In the feature extraction phase scale space representation of objects have been used. Tracking of occluded objects is performed by Kalman filter.

scholarly journals Moving-Object Tracking with In-Vehicle Multi-Laser Range Sensors

2008 ◽  
Vol 20 (3) ◽  
pp. 367-377 ◽  
Author(s):  
Masafumi Hashimoto ◽  
◽  
Yosuke Matsui ◽  
Kazuhiko Takahashi ◽  
Keyword(s):  
Kalman Filter ◽  
Object Tracking ◽  
Moving Objects ◽  
Tracking System ◽  
Moving Object ◽  
Experimental Result ◽  
Coordinate Frame ◽  
Laser Range ◽  
Assignment Algorithm ◽  
Moving Object Tracking

This paper presents a method for moving-object tracking with in-vehicle 2D laser range sensor (LRS) in a cluttered environment. A sensing area of one LRS is limited in orientation, and hence the mobile robot is equipped with multi-LRSs for omnidirectional sensing. Since each LRS takes the laser image on its own local coordinate frame, the laser image is mapped onto a reference coordinate frame so that the object tracking can be achieved by cooperation of multi-LRSs. For mapping the coordinate frames of multi-LRSs are calibrated, that is, the relative positions and orientations of the multi-LRSs are estimated. The calibration is based on Kalman filter and chi-hypothesis testing. Moving-object tracking is achieved by two steps: detection and tracking. Each LRS finds moving objects from its own laser image via a heuristic rule and an occupancy grid based method. It tracks the moving objects via Kalman filter and the assignment algorithm based data association. When the moving objects exist in the overlapped sensing areas of the LRSs, these LRSs exchange the tracking data and fuse them in a decentralized manner. A rule based track management is embedded into the tracking system in order to enhance the tracking performance. The experimental result of three walking-people tracking in an indoor environment validates the proposed method.

GPS and GSM Based Tracking System for Objects Moving over Wide Geographical Areas

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Fatima Ameen ◽  
Ziad Mohammed ◽  
Abdulrahman Siddiq
Keyword(s):  
Global Positioning System ◽  
Mobile Communications ◽  
Moving Objects ◽  
Tracking System ◽  
Geographical Area ◽  
Tracking Systems ◽  
Acceptable Result ◽  
Internet Service ◽  
Global Positioning ◽  
Tracking Objects

Tracking systems of moving objects provide a useful means to better control, manage and secure them. Tracking systems are used in different scales of applications such as indoors, outdoors and even used to track vehicles, ships and air planes moving over the globe. This paper presents the design and implementation of a system for tracking objects moving over a wide geographical area. The system depends on the Global Positioning System (GPS) and Global System for Mobile Communications (GSM) technologies without requiring the Internet service. The implemented system uses the freely available GPS service to determine the position of the moving objects. The tests of the implemented system in different regions and conditions show that the maximum uncertainty in the obtained positions is a circle with radius of about 16 m, which is an acceptable result for tracking the movement of objects in wide and open environments.

scholarly journals A Two-Stage Data Association Approach for 3D Multi-Object Tracking

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2894
Author(s):  
Minh-Quan Dao ◽  
Vincent Frémont
Keyword(s):  
Object Detection ◽  
Object Tracking ◽  
Moving Objects ◽  
Data Association ◽  
Autonomous Driving ◽  
Tracking Accuracy ◽  
Two Stage ◽  
Bipartite Matching ◽  
3D Object ◽  
3D Object Detection

Multi-Object Tracking (MOT) is an integral part of any autonomous driving pipelines because it produces trajectories of other moving objects in the scene and predicts their future motion. Thanks to the recent advances in 3D object detection enabled by deep learning, track-by-detection has become the dominant paradigm in 3D MOT. In this paradigm, a MOT system is essentially made of an object detector and a data association algorithm which establishes track-to-detection correspondence. While 3D object detection has been actively researched, association algorithms for 3D MOT has settled at bipartite matching formulated as a Linear Assignment Problem (LAP) and solved by the Hungarian algorithm. In this paper, we adapt a two-stage data association method which was successfully applied to image-based tracking to the 3D setting, thus providing an alternative for data association for 3D MOT. Our method outperforms the baseline using one-stage bipartite matching for data association by achieving 0.587 Average Multi-Object Tracking Accuracy (AMOTA) in NuScenes validation set and 0.365 AMOTA (at level 2) in Waymo test set.

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