Exploring Context to Learn Scene Specific Object Detectors

Generic person detection is an ill-posed problem as context is widely ignored. Local context can be used to split the generic detection task into easier sub-problems, which was recently explored by classifier grids. The detection problem gets simplified spatially by training separate classifiers for each possible location in the image. So far, adaptive grid based approaches only focused on exploring the specific background class. In contrast, we propose a method using different types of context in order to collect scene specific samples from both, the background and the object class over time. These samples are used to update the specific object detectors. Due to limiting label noise and avoiding direct feedback loops our system can robustly adapt to the scene without drifting. Results on the PETS 2009 dataset show significantly improved person detections, especially, during static and dynamic occlusions (e.g., lamp poles and crowded scenes).