Inverse Multiple Instance Learning for Classifier Grids

Recently, classifier grids have shown to be a considerable alternative for object detection from static cameras. However, one drawback of such approaches is drifting if an object is not moving over a long period of time. Thus, the goal of this work is to increase the recall of such classifiers while preserving their accuracy and speed. In particular, this is realized by adapting ideas from Multiple Instance Learning within a boosting framework. Since the set of positive samples is well defined, we apply this concept to the negative samples extracted from the scene: Inverse Multiple Instance Learning. By introducing temporal bags, we can ensure that each bag contains at least one sample having a negative label, providing the required stability. The experimental results demonstrate that using the proposed approach state-of-the-art detection results can by obtained, however, showing superior classification results in presence of non-moving objects.