Research on pedestrian detection based on Semi-Supervised learning

摘要

In order to implement effective detection and utilize large numbers of unlabeled samples, a pedestrian detection method based on Semi-Supervised learning was presented in this paper. Firstly, BP neural networks classifier, SVM classifier and KNN classifier were selected as the three subclassifiers, and then, the Co-Training mechanism was adopted to train each classifier. Rich information strategy and assistant learning strategy were added in to remove the wrong-marked samples and improve the accuracy of the algorithm by making the most of unlabeled samples. Through the experiments on the test set and real time videos, the feasibility and effectiveness of the approach are verified well.