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This paper presents a pedestrian detection approach that uses neural features from a fully convolutional network (FCN) instead of features manually designed. We train an AdaBoost detector per layer and compare the performance to find the optimal layer for this task. Combining results of multiple detectors can further improve the performance. In order to adapt the FCN to pedestrian detection task,...
Detecting and recognizing traffic signs is a hot topic in the field of computer vision with lots of applications, e.g., safe driving, path planning, robot navigation etc. We propose a novel framework with two deep learning components including fully convolutional network (FCN) guided traffic sign proposals and deep convolutional neural network (CNN) for object classification. Our core idea is to use...
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