Aiming at the problem of high complexity of generic detector and large workload of manual labeling,a new transfer learning framework is proposed,which combines sparse c oding and background subtraction to detect pedestrians.Firstly,the HOG+SVM generic detector is o ptimized,BCLBP and HOG are merged to extract the features and train linear SVM.The GMM-based ba ckground subtraction method is used to obtain the motion region of the frame target samp les to enrich the sample features.Secondly,use the filters including size to filter out some s amples from the target sample as the target template,and then calculate the correlation between the source sample and the target sample and the target template by sparse coding.According to the sparse coefficient and confidence value,calculate the weight of source samples and target samples.I n the process of retraining,all the samples are with allocated weight based on sparse coding,and the abnormal samples of the source samples are excluded,so as to solve the target sample drift and obtain t he pedestrian detector of a specific scene.In order to verify the validity of the algorithm,experiments on the INRIA,Caltech and TUD datasets show that the detection rate of the pedestrian detector in our spec ific scene is improved to some extent compared with other traditional methods.