Context Driven Focus of Attention for Object Detection

Context plays an important role in general scene perception. In particular, it can provide cues about an object’s location within an image. In computer vision, object detectors typically ignore this information. We tackle this problem by presenting a concept of how to extract and learn contextual information from examples. This context is then used to calculate a focus of attention, that represents a prior for object detection. State-of-the-art local appearance-based object detection methods are then applied on selected parts of the image only. We demonstrate the performance of this approach on the task of pedestrian detection in urban scenes using a demanding image database. Results show that context awareness provides complementary information over pure local appearance-based processing. In addition, it cuts down the search complexity and increases the robustness of object detection.