Real-time tracking of multiple objects using adaptive correlation filters with complex constraints

2013 ◽  
Vol 309 ◽  
pp. 265-278 ◽  
Author(s):  
Victor H. Diaz-Ramirez ◽  
Viridiana Contreras ◽  
Vitaly Kober ◽  
Kenia Picos
Keyword(s):  
Real Time ◽  
Correlation Filters ◽  
Multiple Objects ◽  
Complex Constraints ◽  
Real Time Tracking

scholarly journals Real-time tracking of multiple objects with locally adaptive correlation filters

2017 ◽  
Author(s):  
Alexey N. Ruchay ◽  
◽  
Vitaly I. Kober ◽  
Ilya E. Chernoskulov ◽  
◽  
...  
Keyword(s):  
Real Time ◽  
Correlation Filters ◽  
Multiple Objects ◽  
Locally Adaptive ◽  
Real Time Tracking

Robust Real-Time Tracking of Multiple Objects by Volumetric Mass Densities

2013 ◽  
Author(s):  
Horst Possegger ◽  
Sabine Sternig ◽  
Thomas Mauthner ◽  
Peter M. Roth ◽  
Horst Bischof
Keyword(s):  
Real Time ◽  
Multiple Objects ◽  
Real Time Tracking

scholarly journals A Robust Algorithm for Real Time Tracking With Optical Flow

2019 ◽  
Vol 9 (1) ◽  
pp. 887-892
Keyword(s):  
Optical Flow ◽  
Object Tracking ◽  
Real Time ◽  
Traffic Monitoring ◽  
Moving Object ◽  
Computational Time ◽  
Multiple Objects ◽  
Flow Information ◽  
Data Processor ◽  
Real Time Tracking

Object tracking is a troublesome undertaking and significant extent in data processor perception and image handling community. Some of the applications are protection surveillance, traffic monitoring on roads, offense detection and medical imaging. In this paper a recent technique for tracking of moving object is intended. Optical flow information authorizes us to know the displacement and speed of objects personate in a scene. Apply optical flow to the image gives flow vectors of the points to distinguishing the moving aspects. Optical flow is accomplished by Lucas canade algorithm. This algorithm is superior to other algorithms. The outcomes reveals that the intend algorithm is efficient and accurate object tracking method. This paper depicts a smoothing algorithm to track the moving object of both single and multiple objects in real time. The main issue of high computational time is greatly reduced in this proposed work

A Multi-Object Tracking Algorithm Based on Multi-Camera

2011 ◽  
Vol 135-136 ◽  
pp. 70-75
Author(s):  
Ming Xin Jiang ◽  
Hong Yu Wang ◽  
Chao Lin
Keyword(s):  
Computer Vision ◽  
Object Tracking ◽  
Real Time ◽  
Tracking Algorithm ◽  
Location Information ◽  
Multiple Cameras ◽  
Multiple Objects ◽  
Vanishing Points ◽  
Basic Aspect ◽  
Real Time Tracking

As a basic aspect of computer vision, reliable tracking of multiple objects is still an open and challenging issue for both theory studies and real applications. A novel multi-object tracking algorithm based on multiple cameras is proposed in this paper. We obtain the foreground likelihood maps in each view by modeling the background using the codebook algorithm. The view-to-view homographies are computed using several landmarks on the chosen plane. Then, we achieve the location information of multi-target at chest layer and realize the tracking task. The proposed algorithm does not require detecting the vanishing points of cameras, which reduces the complexity and improves the accuracy of the algorithm. The experimental results show that our method is robust to the occlusion and could satisfy the real-time tracking requirement.

scholarly journals Real-time embedded object detection and tracking system in Zynq SoC

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Qingbo Ji ◽  
Chong Dai ◽  
Changbo Hou ◽  
Xun Li
Keyword(s):  
Real Time ◽  
Single Shot ◽  
Correlation Filters ◽  
Detection Model ◽  
Embedded Platform ◽  
Time Performance ◽  
Detection And Tracking ◽  
Complex Scenes ◽  
Real Time Tracking ◽  
Kernel Correlation

AbstractWith the increasing application of computer vision technology in autonomous driving, robot, and other mobile devices, more and more attention has been paid to the implementation of target detection and tracking algorithms on embedded platforms. The real-time performance and robustness of algorithms are two hot research topics and challenges in this field. In order to solve the problems of poor real-time tracking performance of embedded systems using convolutional neural networks and low robustness of tracking algorithms for complex scenes, this paper proposes a fast and accurate real-time video detection and tracking algorithm suitable for embedded systems. The algorithm combines the object detection model of single-shot multibox detection in deep convolution networks and the kernel correlation filters tracking algorithm, what is more, it accelerates the single-shot multibox detection model using field-programmable gate arrays, which satisfies the real-time performance of the algorithm on the embedded platform. To solve the problem of model contamination after the kernel correlation filters algorithm fails to track in complex scenes, an improvement in the validity detection mechanism of tracking results is proposed that solves the problem of the traditional kernel correlation filters algorithm not being able to robustly track for a long time. In order to solve the problem that the missed rate of the single-shot multibox detection model is high under the conditions of motion blur or illumination variation, a strategy to reduce missed rate is proposed that effectively reduces the missed detection. The experimental results on the embedded platform show that the algorithm can achieve real-time tracking of the object in the video and can automatically reposition the object to continue tracking after the object tracking fails.

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