A thermal Object Tracking benchmark

Short-term single-object (STSO) tracking in thermal images is a challenging problem relevant in a growing number of applications. In order to evaluate STSO tracking algorithms on visual imagery, there are de facto standard benchmarks. However, we argue that tracking in thermal imagery is different than in visual imagery, and that a separate benchmark is needed. The available thermal infrared datasets are few and the existing ones are not challenging for modern tracking algorithms. Therefore, we hereby propose a thermal infrared benchmark according to the Visual Visual Object Tracking (VOT) protocol for evaluation of STSO tracking methods. The benchmark includes the new LTIR dataset containing 20 thermal image sequences which have been collected from multiple sources and annotated in the format used in the VOT Challenge. In addition, we show that the ranking of different tracking principles differ between the visual and thermal benchmarks, confirming the need for the new benchmark.

[1]  Michael Felsberg,et al.  Accurate Scale Estimation for Robust Visual Tracking , 2014, BMVC.

[2]  Michael Arens,et al.  Local Feature Based Person Detection and Tracking Beyond the Visible Spectrum , 2011 .

[3]  Rui Caseiro,et al.  Exploiting the Circulant Structure of Tracking-by-Detection with Kernels , 2012, ECCV.

[4]  Thomas B. Moeslund,et al.  Thermal Tracking of Sports Players , 2014, Sensors.

[5]  Guillaume-Alexandre Bilodeau,et al.  An iterative integrated framework for thermal-visible image registration, sensor fusion, and people tracking for video surveillance applications , 2012, Comput. Vis. Image Underst..

[6]  Huchuan Lu,et al.  Visual tracking via adaptive structural local sparse appearance model , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Margrit Betke,et al.  A Thermal Infrared Video Benchmark for Visual Analysis , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[8]  Yi Wu,et al.  Online Object Tracking: A Benchmark , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Gérard G. Medioni,et al.  Context tracker: Exploring supporters and distracters in unconstrained environments , 2011, CVPR 2011.

[10]  James W. Davis,et al.  Background-subtraction using contour-based fusion of thermal and visible imagery , 2007, Comput. Vis. Image Underst..

[11]  James W. Davis,et al.  A Two-Stage Template Approach to Person Detection in Thermal Imagery , 2005, 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1.

[12]  Huchuan Lu,et al.  Robust object tracking via sparsity-based collaborative model , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Michael Felsberg,et al.  Enhanced Distribution Field Tracking Using Channel Representations , 2013, 2013 IEEE International Conference on Computer Vision Workshops.

[14]  Roland Siegwart,et al.  People detection and tracking from aerial thermal views , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[15]  Rustam Stolkin,et al.  Particle Filter Tracking of Camouflaged Targets by Adaptive Fusion of Thermal and Visible Spectra Camera Data , 2014, IEEE Sensors Journal.

[16]  Uwe D. Hanebeck,et al.  Template matching using fast normalized cross correlation , 2001, SPIE Defense + Commercial Sensing.

[17]  Huseyin Ozkan,et al.  Comparison of infrared and visible imagery for object tracking: Toward trackers with superior IR performance , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[18]  J.M. Ferryman,et al.  PETS Metrics: On-Line Performance Evaluation Service , 2005, 2005 IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance.

[19]  Michael Felsberg,et al.  The Visual Object Tracking VOT2013 Challenge Results , 2013, ICCV 2013.

[20]  Thomas B. Moeslund,et al.  Thermal cameras and applications: a survey , 2013, Machine Vision and Applications.