【摘要】 为了解决行人检测过程中漏检的问题,提出一种将传统检测方法与区域候选网络相结合的方法。运用局部无关通道特征(LDCF)方法对图片进行粗检测,筛选出在训练集上漏检的窗口。采用k均值(k-means)算法对数据集中漏检的目标框进行聚类,得到合适的尺度与长宽比。针对相应的尺度与长宽比训练区域候选网络(RPN),提高粗检测阶段的召回率。利用改进的颜色自相似特征以及简化的卷积网络结构对窗口特征进行更为准确的描述。使用改进的深度网络提取特征,并训练级联分类器,对粗检窗口进行精细判断。在行人检测数据集TUD-Brussels和Caltech上进行实验,得到的平均对数漏检率分别为46%和9%。更多还原

【Abstract】 In order to solve the problem of candidate window leakage in pedestrian detection,a pedestrian detection method combining the traditional detection method with the improved region proposal network is proposed. The method of locally decorrelated channel features(LDCF)is used to carry out rough detection of pedestrians,and then filter out the missing window on the training set. The k-means algorithm is used to cluster the missing target frames in the dataset,and the appropriate scale and aspect ratio are obtained. Aiming at the corresponding scale and aspect ratio training region proposal network(RPN),the recall rate of rough detection stage is improved. The improved color self similar feature and the simplified convolution network structure are used to describe the window features more accurately. The improved deep network is used to extract features,and the cascade classifier is trained,making a fine judgment on the rough candidate window. The log-average miss rate obtained by this method on TUD-Brussels and Caltech datasets are respectively reduced to 46% and 9%.更多还原