IEEE Transactions on Very Large Scale Integration (VLSI) Systems - Special issue on the 1995 IEEE ASIC conference
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Example-Based Learning for View-Based Human Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '92 Proceedings of the Second European Conference on Computer Vision
A General Framework for Object Detection
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Journal of Cognitive Neuroscience
Parallelized Architecture of Multiple Classifiers for Face Detection
ASAP '09 Proceedings of the 2009 20th IEEE International Conference on Application-specific Systems, Architectures and Processors
Development of high-speed and real-time vision platform, H3vision
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Accelerating Viola-Jones Face Detection to FPGA-Level Using GPUs
FCCM '10 Proceedings of the 2010 18th IEEE Annual International Symposium on Field-Programmable Custom Computing Machines
Increased Performace of FPGA-Based Color Classification System
FCCM '10 Proceedings of the 2010 18th IEEE Annual International Symposium on Field-Programmable Custom Computing Machines
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In this paper, we propose a high-speed vision system that can be applied to real-time face tracking at 500 fps using GPU acceleration of a boosting-based face tracking algorithm. By assuming a small image displacement between frames, which is a property of high-frame rate vision, we develop an improved boosting-based face tracking algorithm for fast face tracking by enhancing the Viola---Jones face detector. In the improved algorithm, face detection can be efficiently accelerated by reducing the number of window searches for Haar-like features, and the tracked face pattern can be localized pixel-wise even when the window is sparsely scanned for a larger face pattern by introducing skin color extraction in the boosting-based face detector. The improved boosting-based face tracking algorithm is implemented on a GPU-based high-speed vision platform, and face tracking can be executed in real time at 500 fps for an 8-bit color image of 512 脳 512 pixels. In order to verify the effectiveness of the developed face tracking system, we install it on a two-axis mechanical active vision system and perform several experiments for tracking face patterns.