The computation of optical flow
ACM Computing Surveys (CSUR)
Real-time quantized optical flow
CAMP '95 Proceedings of the Computer Architectures for Machine Perception
How to Use High Speed Reconfigurable FPGA for Real Time Image Processing?
CAMP '00 Proceedings of the Fifth IEEE International Workshop on Computer Architectures for Machine Perception (CAMP'00)
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
Closing the Gap: CPU and FPGA Trends in Sustainable Floating-Point BLAS Performance
FCCM '04 Proceedings of the 12th Annual IEEE Symposium on Field-Programmable Custom Computing Machines
Feature Extraction Using Information-Theoretic Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Sliding Window Operation Optimization for FPGA-Based
FCCM '06 Proceedings of the 14th Annual IEEE Symposium on Field-Programmable Custom Computing Machines
Matched Filter Computation on FPGA, Cell and GPU
FCCM '07 Proceedings of the 15th Annual IEEE Symposium on Field-Programmable Custom Computing Machines
GPU-Accelerated KLT Tracking with Monte-Carlo-Based Feature Reselection
DICTA '08 Proceedings of the 2008 Digital Image Computing: Techniques and Applications
Real-Time Optical Flow Calculations on FPGA and GPU Architectures: A Comparison Study
FCCM '08 Proceedings of the 2008 16th International Symposium on Field-Programmable Custom Computing Machines
Accelerating Compute-Intensive Applications with GPUs and FPGAs
SASP '08 Proceedings of the 2008 Symposium on Application Specific Processors
Robust feature extraction via information theoretic learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Optimized generation of memory structure in compiling window operations onto reconfigurable hardware
ARC'07 Proceedings of the 3rd international conference on Reconfigurable computing: architectures, tools and applications
Feature tracking and matching in video using programmable graphics hardware
Machine Vision and Applications
Adaptable Two-Dimension Sliding Windows on NVIDIA GPUs with Runtime Compilation
SAAHPC '11 Proceedings of the 2011 Symposium on Application Accelerators in High-Performance Computing
Improving KLT in Embedded Systems by Processing Oversampling Video Sequence in Real-Time
RECONFIG '11 Proceedings of the 2011 International Conference on Reconfigurable Computing and FPGAs
A performance and energy comparison of FPGAs, GPUs, and multicores for sliding-window applications
Proceedings of the ACM/SIGDA international symposium on Field Programmable Gate Arrays
Correntropy: Properties and Applications in Non-Gaussian Signal Processing
IEEE Transactions on Signal Processing
A Pitch Detector Based on a Generalized Correlation Function
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Computers
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Computer-vision and signal-processing applications often require feature tracking to identify and track the motion of different objects (features) across a sequence of images. Numerous algorithms have been proposed, but common measures of similarity for real-time usage are either based on correlation, mean-squared error, or sum of absolute differences, which are not robust enough for safety-critical applications. To improve robustness, a recent feature-tracking algorithm called C-Flow uses correntropy from Information Theoretic Learning to significantly improve signal-to-noise ratio. In this paper, we present an FPGA accelerator for C-Flow that is typically 3.6-8.5x faster than a GPU and show that the FPGA is the only device capable of real-time usage for large features. Furthermore, we show the FPGA accelerator is more appropriate for embedded usage, with energy consumption that is 2.5-22x less than the GPU.