Feature Descriptors for Tracking by Detection: a Benchmark

In this paper, we provide an extensive evaluation of the performance of local descriptors for tracking applications. Many different descriptors have been proposed in the literature for a wide range of application in computer vision such as object recognition and 3D reconstruction. More recently, due to fast key-point detectors, local image features can be used in online tracking frameworks. However, while much effort has been spent on evaluating their performance in terms of distinctiveness and robustness to image transformations, very little has been done in the contest of tracking. Our evaluation is performed in terms of distinctiveness, tracking precision and tracking speed. Our results show that binary descriptors like ORB or BRISK have comparable results to SIFT or AKAZE due to a higher number of key-points.

[1]  J. M. M. Montiel,et al.  ORB-SLAM: A Versatile and Accurate Monocular SLAM System , 2015, IEEE Transactions on Robotics.

[2]  Max A. Viergever,et al.  Efficient and reliable schemes for nonlinear diffusion filtering , 1998, IEEE Trans. Image Process..

[3]  Jean-Michel Morel,et al.  ASIFT: A New Framework for Fully Affine Invariant Image Comparison , 2009, SIAM J. Imaging Sci..

[4]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[5]  Roland Siegwart,et al.  BRISK: Binary Robust invariant scalable keypoints , 2011, 2011 International Conference on Computer Vision.

[6]  Niklas Bergström,et al.  Robust and adaptive keypoint-based object tracking , 2016, Adv. Robotics.

[7]  Joachim Weickert,et al.  From Box Filtering to Fast Explicit Diffusion , 2010, DAGM-Symposium.

[8]  Vibhav Vineet,et al.  Struck: Structured Output Tracking with Kernels , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  P. Jonathon Phillips,et al.  Empirical Evaluation Methods in Computer Vision , 2002 .

[10]  Jan-Michael Frahm,et al.  Comparative Evaluation of Binary Features , 2012, ECCV.

[11]  Cordelia Schmid,et al.  A performance evaluation of local descriptors , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[13]  Darius Burschka,et al.  Adaptive and Generic Corner Detection Based on the Accelerated Segment Test , 2010, ECCV.

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

[15]  Michael J. Black,et al.  A Naturalistic Open Source Movie for Optical Flow Evaluation , 2012, ECCV.

[16]  Leibo Liu,et al.  A 127 fps in full HD accelerator based on optimized AKAZE with efficiency and effectiveness for image feature extraction , 2015, 2015 52nd ACM/EDAC/IEEE Design Automation Conference (DAC).

[17]  Niklas Bergström,et al.  Detecting, segmenting and tracking unknown objects using multi-label MRF inference , 2014, Comput. Vis. Image Underst..

[18]  Roman P. Pflugfelder,et al.  Consensus-based matching and tracking of keypoints for object tracking , 2014, IEEE Winter Conference on Applications of Computer Vision.

[19]  Adrien Bartoli,et al.  Fast Explicit Diffusion for Accelerated Features in Nonlinear Scale Spaces , 2013, BMVC.

[20]  Ethan Rublee,et al.  ORB: An efficient alternative to SIFT or SURF , 2011, 2011 International Conference on Computer Vision.

[21]  Vincent Lepetit,et al.  BRIEF: Binary Robust Independent Elementary Features , 2010, ECCV.

[22]  Tom Drummond,et al.  Machine Learning for High-Speed Corner Detection , 2006, ECCV.

[23]  Adrien Bartoli,et al.  KAZE Features , 2012, ECCV.

[24]  Cordelia Schmid,et al.  A Comparison of Affine Region Detectors , 2005, International Journal of Computer Vision.

[25]  Niklas Bergström,et al.  Robust 3D tracking of unknown objects , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).