To meet the demands of the public security department to search out specific target in complicated real environment, the target re-IDentification (re-ID) technique was introduced in vehicle identification field, and a vehicle re-ID algorithm based on the model of Bag of Visual Words (BoVW) was proposed. Firstly, in order to solve the probloms of occlusion pose change, target size and position difference in images, the improved scales and poses adaptive Part-based One-vs-One Feature (POOF) were extracted. Secondly, a set of visual words was clustered as a vocabulary by using k-means algorithm based on Euclidean distance, and the features of each image (or target) were expressed as the composition of visual vocabularies. Thirdly, the improved Keep It Simple and Straightforward Metric (KISSME) method followed with re-rank step was used to separate the between-classes and within-classes distances. Finally, the result was obtained by using K-Nearest Neighbor ( KNN) method. The experimental results show that the algorithm has 3.85 percentage points increasement of identification rate in feature representation step compared with Bubble Bank (BB) and 3.14 percentage points increase in metric learning step compared with Bayesian face revisited. Furthermore, it is proved that the proposed algorithm is economical in time-consuming and has strong adaptability to target pose change and small portion of occlusion, which further domonstrates that it can adapt to complicated environments.