NDLI: Multi-view tracking using Kalman filter and graph cut

Content Provider	IEEE Xplore Digital Library
Author	Jahanshahi, P. Masoud, A. Moghadam, E.
Copyright Year	2015
Description	Author affiliation: Dept. of Comput., Islamic Azad Univ. Qazvin Branch Electron., Qazvin, Iran (Jahanshahi, P.; Masoud, A.; Moghadam, E.)
Abstract	In this paper, we propose a multi-view approach to detect and track based on graph-cut and Kalman filter algorithms to solve this problem. The first, object appears in the scene be detected as foreground in each view using a background model and background difference. Next, for related between cameras used homographic constraint. Any pixel inside the foreground object in every view will be related by homographies inducted by the reference view. reference view Images converted to binary images by a graph-cut segmentation. This step separated the position of the intersection points from other parts inside reference images. This added step significantly reduce false positives and missed detections due to points noise or when it cannot be guaranteed that a single reference view image will consistently by scene objects. To track, We measurement the average position of the points. The kakman filter provides an optimal estimate of its position at each time step. The filter kalman, the first one is the prediction of the next state estimate using the previous one; the second is the correction of that estimate using the measurement. Experimental results with detailed qualitative analysis are demonstrated in challenging multiview crowded scenes.
Starting Page	1
Ending Page	5
File Size	1538797
Page Count	5
File Format	PDF
e-ISBN	9781479987337
DOI	10.1109/RIOS.2015.7270729
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-04-12
Publisher Place	Iran
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Computer vision Target tracking Multi view tracking Kalman filter Position measurement Graph-cut Cameras Homography Kalman filters
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Towards a local Kalman filter for visual tracking

Motion reconstruction algorithm using multi-view information

Precise High Speed Multi-Target Multi-Sensor Local Positioning System

Multi-target tracking through opportunistic camera control in a resource constrained multimodal sensor network

Multi-object tracking using binary masks

Image Segmentation Using Fuzzy C-Means Clustering Algorithm Using Kalman Filter

Kalman filtering in position control using a vision sensor

Collaborative Kalman filters for vehicle tracking

Object Tracking Based on Genetic Algorithm and Kalman Filter

Multi-view tracking using Kalman filter and graph cut

Similar Documents

Towards a local Kalman filter for visual tracking

Motion reconstruction algorithm using multi-view information

Precise High Speed Multi-Target Multi-Sensor Local Positioning System

Multi-target tracking through opportunistic camera control in a resource constrained multimodal sensor network

Multi-object tracking using binary masks

Image Segmentation Using Fuzzy C-Means Clustering Algorithm Using Kalman Filter

Kalman filtering in position control using a vision sensor

Collaborative Kalman filters for vehicle tracking

Object Tracking Based on Genetic Algorithm and Kalman Filter

Multi-view tracking using Kalman filter and graph cut