Abstract: Based on the multi-scale analysis, the complex features were calculated with the sliding windows in many target detection and recognition algorithms. However the efficiency was low. Aiming to promote the efficiency, the objectness was introduced to pre-analyze the potential location of objects. While the KINECT was widespread, an objectness methods for the depth image was proposed to leverage the efficiency of other algorithms in the depth map. Firstly, the edge information was extracted from the normal vector of the depth images. Secondly, SVM was used to classify the object according to the score of the objectness. Finally, the different weights were learned for the different scales based on the visual mechanism. The comparative experiment results in the public database demonstrated that the recall rate of our method achieved 94.1% with 1000 proposals. It can leverage the efficiency of the target detection and recognition because of a few candidates with high detection rate.