Integration method for depth feature and traditional feature based on AdaRank

The invention relates to an integration method for depth feature and traditional feature based on AdaRank. The main technical characteristics comprise: dividing image data, respectively establishing aimed at different parts and training a depth convolution and a neural network, used to obtain depth features; extracting traditional features from pedestrian re-identification data, including LOMO features, ELF6 features, and Hog3D features; selecting the following three metric learning methods, KISSME, kLFDA, and LMNN; all the features and the three metric learning methods being combined and spanned to a Cartesian product, to obtain a series of weak sorters; using an AdaRank algorithm, performing ensemble learning on the weak sorters, to finally obtain a strong sorter. The method is reasonable in design, and combines depth learning, multi-feature, metric learning, and ensemble learning, and learns in an integrated manner through establishing the weak sorters, so integrated performance of a system is much better