现有的感兴趣区域( ＲoI) 提取方法很难兼顾较高召回率和较少的 ＲoI 数量． 为了 降低计算开销和 ＲoI 数量，提高召回率，文中提出了适合车载热成像行人检测的 ＲoI 提取 方法:首先，根据行人边缘特征存在的方向差异性判断图像中可能的行人竖直边缘，增强 其幅值;接着，级联行人尺寸约束和自适应局部双阈值分割方法过滤滑窗产生的边界框， 滤除大量的非行人边界框;然后，根据行人的轮廓特征，采用 T 型模板对过滤后的边界框 进行得分评估，在保留可能的行人腿部信息的同时去除边界框内部的无关边缘; 最后，利 用行人的强尺寸约束重新排序 ＲoI，以便在提取固定数量的 ＲoI 时能提高召回率． 在热成 像行人检测数据集 SCUT DataSet 上进行对比实验，结果表明: 当每幅图像提取400 个 ＲoI 时，文中方法的召回率达92%，比 EdgeBox 方法的召回率提高21%，计算时间减少了 10%

Current methods of extracting regions of interest are difficult to balance the high recall rate and the number of ＲoIs． To reduce the amount of computation and ＲoI，and increase recall rate，a ＲoIs extraction method suitable for robust vehicle thermal imaging pedestrian detection was proposed． First，the vertical edges of possible pedestrians in the image were judged according to the directional differences of pedestrian edge features，and their amplitude was enhanced． Then，the boundary boxes generated by sliding windows were filtered by cascade human dimension constraint and adaptive local double threshold segmentation method，and a large number of non-human boundary boxes were removed． Next，according to the outline characteristics of pedestrian，the filtered bounding boxes’score were evaluated by using a T-shaped template，and the unrelated edges inside the bounding boxes were removed while preserving possible pedestrians’leg information． Finally，the ＲoIs were reordered with the strong size constraint of pedestrians，so that the recall rate could be improved when a fixed amount of ＲoI was extracted． A contrast experiment was conducted on a thermal imaging pedestrian detection dataset called SCUT DataSet． The results show that when 400 ＲoIs are extracted from each image，the recall rate of our method reaches 92%; compared with the EdgeBox method，the recall rate increases by 21%，and the calculation time decreases by 10%．