@conference {13246,
	title = {Modelling pedestrian shapes for outlier detection: a neural net based approach},
	booktitle = {Intelligent Vehicles Symposium, 2003. Proceedings. IEEE},
	year = {2003},
	month = {2003/06//},
	pages = {428 - 433},
	abstract = {In this paper we present an example-based approach to learn a given class of complex shapes, and recognize instances of that shape with outliers. The system consists of a two-layer custom-designed neural network. We apply this approach to the recognition of pedestrians carrying objects from a single camera. The system is able to capture and model an ample range of pedestrian shapes at varying poses and camera orientations, and achieves a 90\% correct recognition rate.},
	keywords = {(artificial, complex, Computer, computing;, custom, design;, detection;, engineering, intelligence);, layer, learning, method;, modelling;, net;, nets;, neural, object, outlier, pedestrian, pedestrians, rate;, recognition, recognition;, SHAPE, shapes;, traffic, two, vision;},
	doi = {10.1109/IVS.2003.1212949},
	author = {Nanda,H. and Benabdelkedar,C. and Davis, Larry S.}
}
