Probabilistic Combination of Visual Context Based Attention and Object Detection

Visual context provides cues about an object's presence, position and size within the observed scene, which are used to increase the performance of object detection techniques. However, state-of-the-art methods for context aware object detection could decrease the initial performance. We discuss the reasons for failure and propose a concept that overcomes these limitations. Therefore, we introduce the prior probability function of an object detector, that maps the detector's output to probabilities. Together, with an appropriate contextual weighting a probabilistic framework is established. In addition, we present an extension to state-of-the-art methods to learn scale-dependent visual context information and show how this increases the initial performance. The standard methods and our proposed extensions are compared on a novel demanding image data set.