RIS ID
24096
Abstract
In this paper, we present a biologically inspired method for detecting pedestrians in images. The method is based on a convolutional neural network architecture, which combines feature extraction and classification. The proposed network architecture is much simpler and easier to train than earlier versions. It differs from its predecessors in that the first processing layer consists of a set of pre-defined nonlinear derivative filters for computing gradient information. The subsequent processing layer has trainable shunting inhibitory feature detectors, which are used as inputs to a pattern classifier. The proposed pedestrian detection system is evaluated on the DaimlerChrysler pedestrian classification benchmark database and its performance is compared to the performance of support vector machines and Adaboost classifiers.
Publication Details
This conference paper was originally published as Tivive, FHC & Bouzerdoum, A, A Biologically Inspired Visual Pedestrian Detection System, 2008 International Joint Conference on Neural Networks (IJCNN 2008), Hong Kong, 1-6 June 2008, 703-709. Copyright Institute of Electrical and Electronics Engineers 2008. Original conference paper available here