Discriminative Hough Forests for Object Detection

Object detection models based on the Implicit Shape Model (ISM) [3] use small, local parts that vote for object centers in images. Since these parts vote completely independently from each other, this often leads to false-positive detections due to random constellations of parts. Thus, we introduce a verification step, which considers the activations of all voting elements that contribute to a detection. The levels of activation of each voting element of the ISM form a new description vector for an object hypothesis, which can be examined in order to discriminate between correct and incorrect detections. In particular, we observe the levels of activation of the voting elements in Hough Forests [2], which can be seen as a variant of ISM. In Hough Forests, the voting elements are all the positive training patches used to train the Forest. Each patch of the input image is classified by all decision trees in the Hough Forest. Whenever an input patch falls into the same leaf node as a patch from training, a certain amount of weight is added to the detection hypothesis at the relative position of the object center, which was recorded when cropping out the training patch. The total amount of weight one voting element (offset vector) adds to a detection hypothesis (the total activation) can be calculated by summing over all input patches and trees in the forest. Stacking the activations of all elements gives an activation vector for a hypothesis. We learn classifiers to discriminate correct and wrong part constellations based on these activation vectors and thus assign a better confidence to each detection. We use linear models as well as a histogram intersection kernel SVM. In the linear classifier, one weight is learned for each voting element. We additionally show how to use these weights, not only as a post processing step, but directly in the voting process. This has two advantages: First, it circumvents the explicit calculation of the activation vector for later reclassification, which is computationally more demanding. Second, the non-maxima suppression is performed on cleaner Hough maps, which allows for reducing the size of the suppression neighborhood and thus increases the recall at high levels of precision.