FPGA-based real-time visual tracking system using adaptive color histograms

Visual tracking using a color feature is based on pattern matching algorithms where the appearance of the target is compared with a reference model in successive images and the position of the target is estimated. The major drawback of these methods is that such operations are usually considered at the top level of image processing both due to the data's intrinsic complexity and to the high computational cost associated with a solution in real time. The probabilistic tracking methods have been shown to be robust and versatile for a modest computational cost. However, the probabilistic tracking methods break down easily when the object moves very fast because these methods search only the regions of interest (ROIs) based on the probability density function (pdf) to estimate the position of the moving object. In this paper, we propose a real-time visual tracking circuit using adaptive color histograms. We propose a window- based image processing structure to improve the processing speed of the visual tracking circuit. The visual tracking circuit searches all regions of the image to perform a matching operation in order to estimate the position of the moving object. The main results of our work are that we have designed and implemented a physically feasible hardware circuit to improve the processing speed of the operations required for real-time visual tracking. Therefore, this work has resulted in the development of a real-time visual tracking system employing an FPGA (field programmable gate array) implemented circuit designed by VHDL (the VHSIC hardware description language). Its performance has been measured to compare with the equivalent software implementation.

[1]  John Iselin Woodfill,et al.  Tyzx DeepSea High Speed Stereo Vision System , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[2]  Ben J. A. Kröse,et al.  An EM-like algorithm for color-histogram-based object tracking , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[3]  Larry S. Davis,et al.  Multiple vehicle detection and tracking in hard real-time , 1996, Proceedings of Conference on Intelligent Vehicles.

[4]  Tao Xiong,et al.  Monte Carlo Visual Tracking Using Color Histograms and a Spatially Weighted Oriented Hausdorff Measure , 2003, CAIP.

[5]  Alexandros Eleftheriadis,et al.  Automatic face location detection and tracking for model-assisted coding of video teleconferencing sequences at low bit-rates , 1995, Signal Process. Image Commun..

[6]  Katja Nummiaro A Color-based Particle Filter , 2002 .

[7]  Gary R. Bradski,et al.  Real time face and object tracking as a component of a perceptual user interface , 1998, Proceedings Fourth IEEE Workshop on Applications of Computer Vision. WACV'98 (Cat. No.98EX201).

[8]  Patrick Pérez,et al.  Color-Based Probabilistic Tracking , 2002, ECCV.

[9]  Wolfgang Straßer,et al.  Adaptive Probabilistic Tracking Embedded in a Smart Camera , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[10]  José Santos-Victor,et al.  Robust visual tracking by an active observer , 1997, Proceedings of the 1997 IEEE/RSJ International Conference on Intelligent Robot and Systems. Innovative Robotics for Real-World Applications. IROS '97.

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

[12]  Branko Ristic,et al.  A color-based particle filter for joint detection and tracking of multiple objects , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[13]  Peter F. Sturm,et al.  Adaptive Tracking of Non-Rigid Objects Based on Color Histograms and Automatic Parameter Selection , 2005, 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1.