Parallel implementation of color-based particle filter for object tracking in embedded systems

Recently, embedded systems have become popular because of the rising demand for portable, low-power devices. A common task for these devices is object tracking, which is an essential part of various applications. Until now, object tracking in video sequences remains a challenging problem because of the visual properties of objects and their surrounding environments. Among the common approaches, particle filter has been proven effective in dealing with difficulties in object tracking. In this research, we develop a particle filter based object tracking method using color distributions of video frames as features, and deploy it in an embedded system. Because particle filter is a high-complexity algorithm, we utilize computing power of embedded systems by implementing a parallel version of the algorithm. The experimental results show that parallelization can enhance the performance of particle filter when deployed in embedded systems.

[1]  Tobias Bjerregaard,et al.  A survey of research and practices of Network-on-chip , 2006, CSUR.

[2]  Anton Varfolomieiev,et al.  An improved algorithm of median flow for visual object tracking and its implementation on ARM platform , 2013, Journal of Real-Time Image Processing.

[3]  Michael Isard,et al.  ICONDENSATION: Unifying Low-Level and High-Level Tracking in a Stochastic Framework , 1998, ECCV.

[4]  Ghalem Belalem,et al.  Feasibility Study of a Distributed and Parallel Environment for Implementing the Standard Version of AAM Model , 2016, J. Inf. Process. Syst..

[5]  Jiri Matas,et al.  Robust scale-adaptive mean-shift for tracking , 2013, Pattern Recognit. Lett..

[6]  Ying Wu,et al.  Robust Visual Tracking by Integrating Multiple Cues Based on Co-Inference Learning , 2004, International Journal of Computer Vision.

[7]  Luc Van Gool,et al.  Object Tracking with an Adaptive Color-Based Particle Filter , 2002, DAGM-Symposium.

[8]  Erkan Bostanci,et al.  Augmented reality applications for cultural heritage using Kinect , 2015, Human-centric Computing and Information Sciences.

[9]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.

[10]  Fadi Dornaika,et al.  Developing Vision-based and Cooperative Vehicular Embedded Systems for Enhancing Road Monitoring Services , 2015, ANT/SEIT.

[11]  Andrew Blake,et al.  A Probabilistic Exclusion Principle for Tracking Multiple Objects , 2000, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[12]  Balqies Sadoun,et al.  The BAU GIS system using open source mapwindow , 2015, Human-centric Computing and Information Sciences.

[13]  Oleg A. Stepanov,et al.  Cramér–Rao lower bound in nonlinear filtering problems under noises and measurement errors dependent on estimated parameters , 2016, Autom. Remote. Control..

[14]  Quentin Barthelemy,et al.  Color Sparse Representations for Image Processing: Review, Models, and Prospects , 2015, IEEE Transactions on Image Processing.

[15]  Petar M. Djuric,et al.  Resampling Methods for Particle Filtering: Classification, implementation, and strategies , 2015, IEEE Signal Processing Magazine.

[16]  Fei Hui,et al.  Multiple Vehicle Detection and Tracking in Highway Traffic Surveillance Video Based on SIFT Feature Matching , 2016, J. Inf. Process. Syst..

[17]  F. Dellaert,et al.  A Rao-Blackwellized particle filter for EigenTracking , 2004, CVPR 2004.

[18]  Mohan M. Trivedi,et al.  Embedded Computing Framework for Vision-Based Real-Time Surround Threat Analysis and Driver Assistance , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[19]  Simone Calderara,et al.  Visual Tracking: An Experimental Survey , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Laurent Fiack,et al.  Embedded and real-time architecture for bio-inspired vision-based robot navigation , 2015, Journal of Real-Time Image Processing.