Detection of Pedestrian Crossings with Hierarchical Learning Classifier from Multi-angle Low Altitude Images

This paper proposes a new training method for feature-based iterative hierarchical learning classifiers. It can be used to detect pedestrian crossings from multi-angle low altitude images. The training procedure and the method for merging multi-angle detection results are introduced in this paper. The performance of the classifier was evaluated based on random testing results. Experimental results from several datasets show that the iterative classifier has higher correctness, lower missing rate and lower error rate than the general classifier. Furthermore, the proposed method will not reduce the detection speed.