NDLI: Harnessing feedback region proposals for multi-object tracking

Content Provider	IET Digital Library
Author	Kumar, Aswathy Prasanna Mishra, Deepak
Abstract	In the tracking-by-detection approach of online multiple object tracking (MOT), a major challenge is how to associate object detections on the new video frame with previously tracked objects. Two important aspects that directly influence the performance of MOT are quality of detection and accuracy in data association. The authors propose an efficient and unified MOT framework for improved object detection, followed by enhanced object tracking. The object detection and tracking are considered as two independent functions in the tracking-by-detection paradigm. In this study, object detection accuracy has been increased by employing a faster region-based convolutional neural network (Faster R-CNN) modified with the feedback region proposals from the tracker. Target association is performed by the correlation filter-based Siamese CNN model, which finds the similarity score between the input image patches. The Siamese CNN is trained using a supervised hard sample mining strategy. An optical flow-based motion model is employed to predict the next probable location of the targets from the tracker and these region proposals are fed back to the classifier module of Faster R-CNN. The authors’ extensive analysis of publicly available MOT benchmark datasets and comparison with the state-of-the-art tracking methods demonstrate competitive tracking performance of the proposed MOT framework.
Starting Page	434
Ending Page	442
Page Count	9
ISSN	17519632
Volume Number	14
e-ISSN	17519640
Issue Number	Issue 7, Oct (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/14/7
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2019.0943
Journal	IET Computer Vision
Publisher Date	2020-07-14
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computer Vision And Image Processing Technique Convolutional Neural Nets Correlation Filter-based Siamese CNN Model Correlation Method Data Association Faster R-CNN Faster Region-based Convolutional Neural Network Feedback Feedback Region Proposals Filtering Method in Signal Processing Image Filtering Image Motion Analysis Image Sequence MOT Framework Multiobject Tracking Multiple Object Tracking Neural Computing Technique Object Detection Object Tracking Optical Flow-based Motion Model Optical, Image And Video Signal Processing Target Association Tracking-by-detection Paradigm Video Frame Video Signal Processing
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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

Correlation tracking via robust region proposals

Aircraft tracking based on fully conventional network and Kalman filter

CNN based localisation of forged region in object-based forgery for HD videos

Correlation filter-based visual tracking via adaptive weighted CNN features fusion

Fast object detection based on binary deep convolution neural networks

Multi-target tracking by enhancing the kernelised correlation filter-based tracker

Online multiple object tracking using confidence score-based appearance model learning and hierarchical data association

Occlusion detection via correlation filters for robust object tracking

Occlusion-robust object tracking based on the confidence of online selected hierarchical features

Harnessing feedback region proposals for multi-object tracking

Similar Documents

Correlation tracking via robust region proposals

Aircraft tracking based on fully conventional network and Kalman filter

CNN based localisation of forged region in object-based forgery for HD videos

Correlation filter-based visual tracking via adaptive weighted CNN features fusion

Fast object detection based on binary deep convolution neural networks

Multi-target tracking by enhancing the kernelised correlation filter-based tracker

Online multiple object tracking using confidence score-based appearance model learning and hierarchical data association

Occlusion detection via correlation filters for robust object tracking

Occlusion-robust object tracking based on the confidence of online selected hierarchical features

Harnessing feedback region proposals for multi-object tracking