Object proposals has become the preprocessing in many vision pipelines especially in object localization systems. While with the development and widespread of proposal localization, it is hard to decide which method is more suitable for object detection. In this paper we provide detailed understanding about differences of five object proposal methods. And we provide a set of evaluation metrics and use these metrics to compare and evaluate the recall situation of these object proposal methods. Using the ground-truth instance for exploring unsupervised object localization on PASCAL VOC 2007, MSRA10K and Object Discovery datasets. Our experimental analysis demonstrates that the performance of these methods depends on the number of candidates and the threshold of recall, meanwhile the methods that generate the candidates with scores perform better than those methods that do not with scores. Our findings show the advantages and disadvantages of these methods, and provide insights and metrics to motivate the evaluation of object proposal methods.