The present invention discloses a pedestrian detection method based on Haar-like intermediate layer filtering features, comprising the steps of: extracting object features of various training images in a training image set, training an Adaboost classifier based on decision trees by using the extracted object feature data to obtain a classification model; extracting object features of an image to be detected under a plurality of scales and inputting the object features to the classification model to obtain a pedestrian detection result, wherein a method for extracting the object features comprises the following steps: respectively extracting a plurality of different channel features of an original image to obtain multiple channel feature patterns of the original image; respectively performing downsampling for each channel feature pattern; respectively extracting corresponding Haar-like features of each channel feature pattern which has been subjected to the downsampling by using a group of preset Haar-like feature templates; and clustering all the Haar-like features of the original image into the object features of the original image. The invention also discloses a pedestrian detection apparatus based on Haar-like intermediate layer filtering features. The pedestrian detection method and the pedestrian detection apparatus can effectively improve pedestrian detection performance.