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.

Real-Time Structure Estimation in Dynamic Scenes Using a Single Camera

2013 ◽  
pp. 173-191
Author(s):  
Ashwin P. Dani ◽  
Zhen Kan ◽  
Nic Fischer ◽  
Warren E. Dixon
Keyword(s):  
Moving Objects ◽  
3D Structure ◽  
Estimation Algorithm ◽  
Unknown Input ◽  
Single Camera ◽  
Time Varying ◽  
Moving Camera ◽  
Structure Estimation ◽  
Modeling Uncertainties ◽  
Exponentially Stable

In this chapter, an online method is developed for estimating 3D structure (with proper scale) of moving objects seen by a moving camera. In contrast to traditionally developed batch solutions for this problem, a nonlinear unknown input observer strategy is used where the object’s velocity is considered as an unknown input to the perspective dynamical system. The estimator is exponentially stable, and hence, provides robustness against modeling uncertainties and measurement noise from the camera. The developed method provides first causal, observer based structure estimation algorithm for a moving camera viewing a moving object with unknown time-varying object velocities.

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 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.

GEO-BASED AERIAL SURVEILLANCE VIDEO PROCESSING FOR SCENE UNDERSTANDING AND OBJECT TRACKING

2009 ◽  
Vol 23 (07) ◽  
pp. 1285-1307
Author(s):  
JIANGJIAN XIAO ◽  
HUI CHENG ◽  
FENG HAN ◽  
HARPREET SAWHNEY
Keyword(s):  
3D Reconstruction ◽  
Object Tracking ◽  
Video Processing ◽  
Moving Objects ◽  
Spatial Information ◽  
Ground Plane ◽  
Scene Understanding ◽  
Video Data ◽  
Surveillance Video ◽  
Aerial Surveillance

This paper presents an approach to extract semantic layers from aerial surveillance videos for scene understanding and object tracking. The input videos are captured by low flying aerial platforms and typically consist of strong parallax from non-ground-plane structures as well as moving objects. Our approach leverages the geo-registration between video frames and reference images (such as those available from Terraserver and Google satellite imagery) to establish a unique geo-spatial coordinate system for pixels in the video. The geo-registration process enables Euclidean 3D reconstruction with absolute scale unlike traditional monocular structure from motion where continuous scale estimation over long periods of time is an issue. Geo-registration also enables correlation of video data to other stored information sources such as GIS (Geo-spatial Information System) databases. In addition to the geo-registration and 3D reconstruction aspects, the other key contributions of this paper also include: (1) providing a reliable geo-based solution to estimate camera pose for 3D reconstruction, (2) exploiting appearance and 3D shape constraints derived from geo-registered videos for labeling of structures such as buildings, foliage, and roads for scene understanding, and (3) elimination of moving object detection and tracking errors using 3D parallax constraints and semantic labels derived from geo-registered videos. Experimental results on extended time aerial video data demonstrates the qualitative and quantitative aspects of our work.

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 Ballistic Target Tracking with Use of Cinetheodolites

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Piotr Kaniewski ◽  
Piotr Smagowski ◽  
Stanislaw Konatowski
Keyword(s):  
Object Tracking ◽  
Tracking System ◽  
Estimation Algorithm ◽  
Optical Tracking ◽  
Optical Systems ◽  
Trajectory Data ◽  
Optical Tracking System ◽  
Processing Data ◽  
Measurement Equations ◽  
Simulation Results

The paper addresses a problem of ballistic object tracking with the use of the cinetheodolite electro-optical tracking system. Electro-optical systems are applied for acquiring the trajectory data of missiles, satellites, and rockets used for delivery of satellites to their prevised orbits. Despite the importance of such systems and their applications, in the open literature there are no publications describing tracking algorithms processing data from cinetheodolites. The paper describes a model-based algorithm of estimation of position and parameters of target motion for such a system, developed by the authors. The model of the system, nonlinear both in its description of the target dynamics and the measurement equations, is presented in detail. The proposed algorithm of estimation is also described, and chosen simulation results are included in the paper. Furthermore, a comparison of the proposed estimation algorithm with other possible, but simpler algorithms is presented.

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.

scholarly journals Improvement of sub region matching illumination transfer in hybrid shadow removal method for moving vehicle video

2018 ◽  
Vol 7 (4) ◽  
pp. 2678
Author(s):  
Budi Setiyono ◽  
Dwi Ratna Sulistyaningrum ◽  
Soetrisno . ◽  
Hasanuddin Al-Habib
Keyword(s):  
Moving Objects ◽  
Intelligent Transportation System ◽  
Technology Development ◽  
Gaussian Mixture ◽  
Mean Value ◽  
Shadow Removal ◽  
Moving Vehicle ◽  
Shadow Area ◽  
Region Matching ◽  
Transfer Method

Intelligent Transportation System (ITS) is a concept to manage transportation based on technology development. Video from surveillance cameras can be used for monitoring the number of vehicles and speed using digital image processing. Shadows on the vehicle is one of the noise that must be removed in order to obtain better accuracy. Shadow is caused by the reflection of objects exposed to the light. In this study, we combined two methods to eliminate shadows on moving vehicle, the subregion illumination transfer method and the background-based Gaussian mixture model. Foreground image is used for sub-Region Illumination Transfer and gamma decoding processes is used to detect the presence of shadows The detected shadow is removed by replacing it with the background in that position. Experiments are done by making simulated video of moving objects without shadows and objects that have a shadow. By using the proposed method, the shadow will be omitted, and the results are compared with the object without the shadow. The experimental results are: mean value of PSNR for objects moving closer to the camera with a light intensity of 0.8 is 53.47. While on the moving object with a small shadow area, we obtained an average PSNR of 51.87927dB.  

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