A Motion Detection Model Inspired by the Neuronal Propagation in the Hippocampus

Haichao LIANG  Takashi MORIE  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E95-A   No.2   pp.576-585
Publication Date: 2012/02/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E95.A.576
Print ISSN: 0916-8508
Type of Manuscript: PAPER
Category: Vision
edge-based motion detection,  time-to-travel,  approaching object detection,  bio-inspired system,  

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We propose a motion detection model, which is suitable for higher speed operation than the video rate, inspired by the neuronal propagation in the hippocampus in the brain. The model detects motion of edges, which are extracted from monocular image sequences, on specified 2D maps without image matching. We introduce gating units into a CA3-CA1 model, where CA3 and CA1 are the names of hippocampal regions. We use the function of gating units to reduce mismatching for applying our model in complicated situations. We also propose a map-division method to achieve accurate detection. We have evaluated the performance of the proposed model by using artificial and real image sequences. The results show that the proposed model can run up to 1.0 ms/frame if using a resolution of 6460 units division of 320240 pixels image. The detection rate of moving edges is achieved about 99% under a complicated situation. We have also verified that the proposed model can achieve accurate detection of approaching objects at high frame rate (>100 fps), which is better than conventional models, provided we can obtain accurate positions of image features and filter out the origins of false positive results in the post-processing.