Multiple objects tracking with HOGs matching in circular windows

In recent years tracking applications with development of new technologies like smart TVs, Kinect, Google Glass and Oculus Rift become very important. When tracking uses a matching algorithm, a good prediction algorithm is required to reduce the search area for each object to be tracked as well as processing time. In this work, we analyze the performance of different tracking algorithms based on prediction and matching for a real-time tracking multiple objects. The used matching algorithm utilizes histograms of oriented gradients. It carries out matching in circular windows, and possesses rotation invariance and tolerance to viewpoint and scale changes. The proposed algorithm is implemented in a personal computer with GPU, and its performance is analyzed in terms of processing time in real scenarios. Such implementation takes advantage of current technologies and helps to process video sequences in real-time for tracking several objects at the same time.

[1]  James W. Davis,et al.  Real-time closed-world tracking , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[2]  J Campos,et al.  Phase-only filter with improved discrimination. , 1994, Optics letters.

[3]  Andrew W. Fitzgibbon,et al.  Combining local and global motion models for feature point tracking , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[5]  Vitaly Kober,et al.  A fast kernel tracking algorithm based on local gradient histograms , 2013, Optics & Photonics - Optical Engineering + Applications.

[6]  Arnold W. M. Smeulders,et al.  The Amsterdam Library of Object Images , 2004, International Journal of Computer Vision.

[7]  Zhipei Huang,et al.  Multiple object video tracking using GRASP-MHT , 2012, 2012 15th International Conference on Information Fusion.

[8]  Vitaly Kober,et al.  CWMA: Circular Window Matching Algorithm , 2013, CIARP.

[9]  Jouko Lampinen,et al.  Rao-Blackwellized particle filter for multiple target tracking , 2007, Inf. Fusion.

[10]  Joseph JáJá,et al.  An Introduction to Parallel Algorithms , 1992 .

[11]  Paul W. Fieguth,et al.  Color-based tracking of heads and other mobile objects at video frame rates , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[12]  V. Kober,et al.  Pattern recognition in nonoverlapping background with noisy target image , 2010, Pattern Recognition and Image Analysis.

[13]  Vitaly Kober,et al.  Adaptive composite filters for pattern recognition in linearly degraded and noisy scenes , 2008 .

[14]  P. Koumoutsakos,et al.  Feature point tracking and trajectory analysis for video imaging in cell biology. , 2005, Journal of structural biology.

[15]  Vitaly Kober,et al.  Design of correlation filters for pattern recognition with disjoint reference image , 2011 .

[16]  Feng Wu,et al.  Very Fast Template Matching , 2002, ECCV.

[17]  Wilhelm Burger,et al.  Digital Image Processing - An Algorithmic Introduction using Java , 2008, Texts in Computer Science.

[18]  Vitaly Kober,et al.  Real-time tracking of multiple objects using adaptive correlation filters with complex constraints , 2013 .

[19]  Vitaly Kober,et al.  Design of correlation filters for pattern recognition using a noisy reference , 2012 .

[20]  Larry S. Davis,et al.  W4: Real-Time Surveillance of People and Their Activities , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Ishwar K. Sethi,et al.  Finding Trajectories of Feature Points in a Monocular Image Sequence , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Vitaly Kober,et al.  Adaptive composite filters for pattern recognition in nonoverlapping scenes using noisy training images , 2014, Pattern Recognit. Lett..

[23]  Ibrahim Türkoglu,et al.  A hybrid tracking method for scaled and oriented objects in crowded scenes , 2011, Expert Syst. Appl..

[24]  Ming-Hsuan Yang,et al.  Online visual tracking with histograms and articulating blocks , 2010, Comput. Vis. Image Underst..

[25]  X. R. Li,et al.  Survey of maneuvering target tracking. Part I. Dynamic models , 2003 .

[26]  Vitaly Kober,et al.  A fast matching algorithm based on local gradient histograms , 2012, Other Conferences.

[27]  Vitaly Kober,et al.  Multiclass pattern recognition using adaptive correlation filters with complex constraints , 2012 .

[28]  Jake K. Aggarwal,et al.  Temporal spatio-velocity transform and its application to tracking and interaction , 2004, Comput. Vis. Image Underst..

[29]  Yiwei Wang,et al.  Moving object tracking in video , 2000, Proceedings 29th Applied Imagery Pattern Recognition Workshop.