The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

In this paper, we describe a statistical method for 3D object detection. We represent the statistics of both object appearance and "non-object" appearance using a product of histograms. Each histogram represents the joint statistics of a subset of wavelet coefficients and their position on the object. Our approach is to use many such histograms representing a wide variety of visual attributes. Using this method, we have developed the first algorithm that can reliably detect human faces with out-of-plane rotation and the first algorithm that can reliably detect passenger cars over a wide range of viewpoints

Keywords

Pattern Recognition, Computer Vision, Face Detection, Car Detection, Automobile Detection, Statistical Classification, Histogram, Wavelet, AdaBoost, View-Based Modeling

Notes

Associated Center(s) / Consortia: Vision and Autonomous Systems Center

Associated Lab(s) / Group(s): Video Surveillance and Monitoring, People Image Analysis Consortium, Face Group

Associated Project(s): Video Surveillance and Monitoring, Object Recognition Using Statistical Modeling, Face Detection Databases, Face Detection, Face Databases

Text Reference

Henry Schneiderman and Takeo Kanade, "A Statistical Model for 3D Object Detection Applied to Faces and Cars," IEEE Conference on Computer Vision and Pattern Recognition, June, 2000.

BibTeX Reference

@inproceedings{Schneiderman_2000_3294,
   author = "Henry Schneiderman and Takeo Kanade",
   title = "A Statistical Model for 3D Object Detection Applied to Faces and Cars",
   booktitle = "IEEE Conference on Computer Vision and Pattern Recognition",
   publisher = "IEEE",
   month = "June",
   year = "2000",
}

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