基于自学习的行人检测方法

A self-learning framework for pedestrian detection

离线训练的分类器应用于特定场景时,其检测性能将急剧下降.手工标注虽然可以提高检测性能,但是需要耗费大量的人工成本.为此提出一种基于自学习的行人检测方法,该方法可以改变任意离线训练的分类器用于特定场景的行人检测,并且取得了较好的识别率.首先将训练级联分类器作为离线分类器,并使用任意公开的行人图片训练高斯混合模型(GMM);然后利用离线分类测器对特定场景进行行人检测并获取候选对象的置信分数;再根据置信分数的高低构建正负样本集合并使用高斯混合模型重新标识样本;最后使用SVM分类器在线训练行人分类器,对候选对象进行重新预测.在公开和自制数据集上的实验结果表明,该方法提高了通用行人检测器的准确性,并且明显优于传统方法.

The performance of offline trained pedestrian detectors significantly drops when they are applied to the specific scene.Although manual labeling can improve detection performance,it requires a lot of human effort.In this paper,a self-learning framework is proposed for pedestrian detection,which can adapt any offline trained detector to a specific scene and obtain a better performance.Firstly,Cascade classifier is used as an offline classifier,while a Gaussian Mixture Model(GMM)is trained using a set of public pedestrian photos.Next,a low threshold offline classifier is used to perform pedestrian detection on a specific scene and the confidence score of candidate detections is obtained.Then,samples with high confidence scores are selected as positive samples,while those with low confidence scores are taken as a negative samples,and GMM is used to represent the candidate detection again.Finally,a discriminative pedestrian classifier is trained online using the SVM classifier to re-estimate candidate objects.Experimental results on public and self-made datasets show that the proposed method can improve the accuracy of the generic pedestrian detector and significantly outperforms the traditional methods.