This paper presents a trainable object detection architecture that is applied to detecting people in static images of cluttered scenes. This problem poses several challenges. People are highly non-rigid objects with a high degree of variability in size, shape, color, and texture. Unlike previous approaches, this system learns from examples and does not rely on any a priori (hand-crafted) models or on motion. The detection technique is based on the novel idea of the wavelet template that defines the shape of an object in terms of a subset of the wavelet coefficients of the image. It is invariant to changes in color and texture and can be used to robustly define a rich and complex class of objects such as people. We show how the invariant properties and computational efficiency of the wavelet template make it an effective tool for object detection.
CITATION STYLE
Oren, M., Papageorgiou, C., Sinha, P., Osuna, E., & Poggio, T. (1997). Pedestrian detection using wavelet templates. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 193–199). IEEE. https://doi.org/10.1109/cvpr.1997.609319
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