Abstract
We propose a convolutional neural network (CNN)-based approach to detect vehicles in front of an ego-vehicle for road traffic scenes at night and compare it with our AdaBoost-based approaches. The new approach enhances the previous approaches [5] in terms of vehicle detection performance. We also produce negative learning data by exploiting a vehicle candidate-generation scheme that further improves classifier performance. The experimental results for real-world road images illustrate the effectiveness of the proposed algorithm.
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