Boosted local binaries for object detection

We propose a novel binary feature for object detection encoding local neighbor patterns of different sizes and locations. Each region pair of the proposed feature is selected by RealAdaBoost algorithm with a penalty term on the structure diversity. As a result, useful features that are good at describing specific objects will be chosen to build the classifier. Moreover, the encoding scheme is applied in both the gradient domain and the intensity domain, which is complementary to standard binary features (e.g. LBP and LAB). The proposed method was tested using the CMU-MIT frontal face dataset, INRIA pedestrian dataset, and UIUC car dataset respectively. Experimental results show that the proposed method outperforms traditional binary features LBP and LAB, which contributes to a significant improvement on detection accuracy and converges 2 times faster. It also achieves comparable performance with some state-of-the-art algorithms.