The application of cortex visual cognitive model on road environment perception

Rieseshuber and Poggio from MIT proposed a standard quantitative model of cortex visual cognitive. This feedforward hierarchical model perfect follows the visual perception mechanisms of the cortex from simple cells to the complex cells. This paper first adopts this cortex visual cognitive model (CVCM) into the street environment perception algorithm which is the hot issue in the intelligent transportation field. The algorithm works well in typical street objects detecting and localization in cluster street scene by combining the cortex perception model with the scaled windowing technique. The results show excellent detection rate and low false alarm rate.

[1]  PoggioTomaso,et al.  Robust Object Recognition with Cortex-Like Mechanisms , 2007 .

[2]  Zehang Sun,et al.  Quantized wavelet features and support vector machines for on-road vehicle detection , 2002, 7th International Conference on Control, Automation, Robotics and Vision, 2002. ICARCV 2002..

[3]  Pietro Perona,et al.  Unsupervised Learning of Models for Recognition , 2000, ECCV.

[4]  Zehang Sun,et al.  On-road vehicle detection using Gabor filters and support vector machines , 2002, 2002 14th International Conference on Digital Signal Processing Proceedings. DSP 2002 (Cat. No.02TH8628).

[5]  Brian Leung,et al.  Component-based Car Detection in Street Scene Images , 2004 .

[6]  Y. Amit,et al.  An integrated network for invariant visual detection and recognition , 2003, Vision Research.

[7]  T. Poggio,et al.  Hierarchical models of object recognition in cortex , 1999, Nature Neuroscience.

[8]  Prashant Parikh.  A Theory of Communication , 2010 .

[9]  Tian Jin-wen.  Quantitative Model of Visual Cortex and its Application in Object Detection , 2009 .

[10]  Y. LeCun,et al.  Learning methods for generic object recognition with invariance to pose and lighting , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[11]  Dan Roth,et al.  Learning a Sparse Representation for Object Detection , 2002, ECCV.

[12]  Takeo Kanade,et al.  A statistical method for 3D object detection applied to faces and cars , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[13]  Gang Wang,et al.  Using Dependent Regions for Object Categorization in a Generative Framework , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[14]  Trevor Darrell,et al.  The pyramid match kernel: discriminative classification with sets of image features , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[15]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[16]  Tomaso A. Poggio,et al.  A Trainable System for Object Detection , 2000, International Journal of Computer Vision.

[17]  D Marr,et al.  A computational theory of human stereo vision. , 1979, Proceedings of the Royal Society of London. Series B, Biological sciences.

[18]  Jitendra Malik,et al.  SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[19]  A. Aeron-Thomas,et al.  ESTIMATING GLOBAL ROAD FATALITIES , 2000 .

[20]  J. P. Jones,et al.  An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex. , 1987, Journal of neurophysiology.