Full weighting Hough Forests for object detection

Object detection plays an important role in autonomous video surveillance systems nowadays. Models based on the Hough Forests are widely applied, which use the local patches that vote for the object centers in images. Since these patches vote independently from each other, there is no guarantee that trees built in Hough Forests can obtain optimal parameters for the entire model. This paper proposes a novel method to improve the Hough Forests by introducing weights to each offset in the positive training images to specify the importance of the patch to the training object. Also, all patches in the dataset are weighted and updated during the training process by minimizing the global loss function. The weights are used in both the training and detection phases to obtain a more accurate location of instances in detection images. The proposed method is then evaluated on TUD pedestrian and UIUC car datasets showing promising results compared to recent methods such as Hough Forests, and Alternating Decision Forests.