The computation of optical flow
ACM Computing Surveys (CSUR)
Computation and analysis of image motion: a synopsis of current problems and methods
International Journal of Computer Vision
Time-to-Collision Estimation from Motion Based on Primate Visual Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Emergence of sequence sensitivity in a hippocampal CA3-CA1 model
Neural Networks
An FPGA-based CollisionWarning System Using Hybrid Approach
HIS '07 Proceedings of the 7th International Conference on Hybrid Intelligent Systems
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
A comparison of three methods for measure of time to contact
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
A role for signal propagation through the hippocampal CA2 field in memory formation
WImBI'06 Proceedings of the 1st WICI international conference on Web intelligence meets brain informatics
A Database and Evaluation Methodology for Optical Flow
International Journal of Computer Vision
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We have proposed a motion detection model, CA3-GU-CA1 (CGC) model, inspired by hippocampal function. The CGC model treats edges extracted from monocular image sequences, and detects motion of the edges on segmented 2D maps without image matching. In this paper, we propose an FPGA implementation of the CGC model, in order to achieve low power processing toward practical use. Then, we propose an obstacle detection algorithm using time-to-collision (TTC) based edge grouping. We have evaluated the performance of motion and obstacle detection by using artificial and real image sequences. The results show that the CGC model can achieve high detection rate in complicated situations, and can achieve accurate detection when using a high frame-rate. The proposed obstacle-detection algorithm can detect dangerous objects moving across based on a novel TTC estimation algorithm. Both motion detection and obstacle detection parts can operate at more than 1000fps. The CGC model can also operate with a power dissipation of about 1.4W based on the FPGA implementation.