Pedestrian Detection with R-CNN
نویسنده
چکیده
In this paper we evaluate the effectiveness of using a Region-based Convolutional Neural Network approach to the problem of pedestrian detection. Our dataset is composed of manually annotated video sequences from the ETH vision lab. Using selective search as our proposal method, we evaluate the performance of several neural network architectures as well as a baseline logistic regression unit. We find that the best result was split between using the AlexNet architecture with weights pre-trained on ImageNet as well as a variant of this network trained from scratch.
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