Prof. Dr. Didier Stricker
Dr. Alain Pagani
Dr. Gerd Reis
Eric Thil
Keonna Cunningham
Dr. Oliver Wasenmüller
Dr. Gabriele Bleser
Dr. Bruno Mirbach
Dr. Jason Raphael Rambach
Dr. Bertram Taetz
Dr. Muhammad Zeshan Afzal
Sk Aziz Ali
Mhd Rashed Al Koutayni
Murad Almadani
Alaa Alshubbak
Yuriy Anisimov
Jilliam Maria Diaz Barros
Ramy Battrawy
Hammad Butt
Mahdi Chamseddine
Steve Dias da Cruz
Fangwen Shu
Torben Fetzer
Ahmet Firintepe
Sophie Folawiyo
David Michael Fürst
Kamalveerkaur Garewal
Christiano Couto Gava
Leif Eric Goebel
Tewodros Amberbir Habtegebrial
Simon Häring
Khurram Hashmi
Jigyasa Singh Katrolia
Andreas Kölsch
Onorina Kovalenko
Stephan Krauß
Paul Lesur
Muhammad Jameel Nawaz Malik
Michael Lorenz
Markus Miezal
Mina Ameli
Nareg Minaskan Karabid
Mohammad Minouei
Pramod Murthy
Mathias Musahl
Peter Neigel
Manthan Pancholi
Qinzhuan Qian
Engr. Kumail Raza
Dr. Nadia Robertini
María Alejandra Sánchez Marín
Dr. Kripasindhu Sarkar
Alexander Schäfer
Pascal Schneider
René Schuster
Mohamed Selim
Lukas Stefan Staecker
Dennis Stumpf
Yongzhi Su
Xiaoying Tan
Yaxu Xie
Dr. Vladislav Golyanik
Dr. Aditya Tewari
André Luiz Brandão
A Superior Tracking Approach: Building a strong Tracker through Fusion
A Superior Tracking Approach: Building a strong Tracker through Fusion
Computer Vision ECCV 2014 European Conference on Computer Vision (ECCV-14), September 6-12, Zürich, Switzerland
- Abstract:
- General object tracking is a challenging problem, where each tracking algorithm performs well on different sequences. This is because each of them has different strengths and weaknesses. We show that this fact can be utilized to create a fusion approach that clearly outperforms the best tracking algorithms in tracking performance. Thanks to dynamic programming based trajectory optimization we cannot only outperform tracking algorithms in accuracy but also in other important aspects like trajectory continuity and smoothness. Our fusion approach is very generic as it only requires frame-based tracking results in form of the object’s bounding box as input and thus can work with arbitrary tracking algorithms. It is also suited for live tracking. We evaluated our approach using 29 different algorithms on 51 sequences and show the superiority of our approach compared to state-of-the-art tracking methods.
- Keywords:
- Object Tracking, Data Fusion