A Multi-Object Tracking Algorithm Based on Multi-Camera

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.

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An Efficient and Robust Multi-Object Recognition and Tracking Algorithm using Mask Region based Convolution Neural Network (R-CNN)

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.i7569.078919 ◽

2019 ◽

Vol 8 (9) ◽

pp. 607-613

Keyword(s):

Neural Network ◽

Computer Vision ◽

Object Recognition ◽

Object Tracking ◽

Ground Truth ◽

Benchmark Dataset ◽

Tracking Algorithm ◽

Multiple Objects ◽

High Quality ◽

Extensive Simulation

Presently, Multi-Object tracking (MOT) is mainly applied for predicting the positions of many predefined objects across many successive frames with the provided ground truth position of the target in the first frame. The area of MOT gains more interest in the area of computer vision because of its applicability in various fields. Many works have been presented in recent years that intended to design a MOT algorithm with maximum accuracy and robustness. In this paper, we introduce an efficient as well as robust MOT algorithm using Mask R-CNN. The usage of Mask R-CNN effectively identifies the objects present in the image while concurrently creating a high-quality segmentation mask for every instance. The presented MOT algorithm is validated using three benchmark dataset and the results are extensive simulation. The presented tracking algorithm shows its efficiency to track multiple objects precisely

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A Robust Algorithm for Real Time Tracking With Optical Flow

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.a4419.119119 ◽

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

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Security System and Surveillance Using Real Time Object Tracking and Multiple Cameras

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.403-408.4968 ◽

2011 ◽

Vol 403-408 ◽

pp. 4968-4973

Author(s):

Rajendra Kachhava ◽

Vivek Srivastava ◽

Rajkumar Jain ◽

Ekta Chaturvedi

Keyword(s):

Object Detection ◽

Object Tracking ◽

Real Time ◽

Video Surveillance ◽

Research Field ◽

Security System ◽

Multiple Cameras ◽

Multiple Camera ◽

Detection And Tracking ◽

Real Time Tracking

In this paper we propose multiple cameras using real time tracking for surveillance and security system. It is extensively used in the research field of computer vision applications, like that video surveillance, authentication systems, robotics, pre-stage of MPEG4 image compression and user inter faces by gestures. The key components of tracking for surveillance system are extracting the feature, background subtraction and identification of extracted object. Video surveillance, object detection and tracking have drawn a successful increased interest in recent years. A object tracking can be understood as the problem of finding the path (i.e. trajectory) and it can be defined as a procedure to identify the different positions of the object in each frame of a video. Based on the previous work on single detection using single stationary camera, we extend the concept to enable the tracking of multiple object detection under multiple camera and also maintain a security based system by multiple camera to track person in indoor environment, to identify by my proposal system which consist of multiple camera to monitor a person. Present study mainly aims to provide security and detect the moving object in real time video sequences and live video streaming. Based on a robust algorithm for human body detection and tracking in videos created with support of multiple cameras.

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Object Tracking Algorithm Based on Block LAB Feature Histogram and Particle Filter

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.485.193 ◽

2012 ◽

Vol 485 ◽

pp. 193-199

Author(s):

Ming Sun ◽

Jia Wei Li

Keyword(s):

Particle Filter ◽

Object Tracking ◽

Real Time ◽

Moving Objects ◽

Tracking Algorithm ◽

Color Histogram ◽

Complex Environments ◽

Excellent Performance ◽

Real Time Tracking ◽

Tracking Objects

In order to improve real-time object tracking effect when tracking objects are partly covered or mixed by different backgrounds, and even under the conditions of changed illuminations, in this paper, we proposed an object tracking algorithm based on block LAB feature histogram and particle filter. To demonstrate new algorithm’s excellent performance, we carried several compared experiments when objects moved under different conditions such as changed illuminations, mixed backgrounds and so forth. Experiment results show that tracking objects are often lost by using tracking algorithm based on traditional features such as color histogram, but moving objects under various and complex environments could be correctly tracked by using real-time tracking algorithm proposed in this paper.

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Real-Time Object Tracking Algorithm Employing On-Line Support Vector Machine and Multiple Candidate Regeneration

Artificial Intelligence and Soft Computing - Lecture Notes in Computer Science ◽

10.1007/978-3-642-29347-4_72 ◽

2012 ◽

pp. 617-625 ◽

Cited By ~ 2

Author(s):

Pushe Zhao ◽

Renyuan Zhang ◽

Tadashi Shibata

Keyword(s):

Support Vector Machine ◽

Object Tracking ◽

Real Time ◽

Tracking Algorithm ◽

Support Vector ◽

On Line

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Cross-Domain Usage in Real-Time Video-Based Tracking

Advances in Multimedia and Interactive Technologies - Handbook of Research on Advanced Concepts in Real-Time Image and Video Processing ◽

10.4018/978-1-5225-2848-7.ch005 ◽

2017 ◽

pp. 105-129

Author(s):

Satbir Singh ◽

Rajiv Kapoor ◽

Arun Khosla

Keyword(s):

Object Tracking ◽

Real Time ◽

Future Development ◽

Complete Information ◽

Cutting Edge ◽

Edge Field ◽

Cross Domain ◽

Distinct Category ◽

Real Time Tracking ◽

The Cross

This chapter emphasizes on the approach to include information from different type of sensors into the visible domain real time tracking. Since any individual sensor is not able to retrieve the complete information, so it is better to use information from distinct category of sensors. The chapter firstly enlightens the significance of introducing the cross-domain treatment into video based tracking. Following this, some previous work in the literature related to this idea is briefed. The chapter introduces the categorization of the cross-domain activity usage for real time object tracking and then each category is separately discussed in detail. The advantages as well as the limitations of each type of supplemented cross domain activity will be discussed. Finally, the recommendation and concluding remarks from the authors in lieu of future development of this cutting-edge field will be presented.

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