Urban scene based Semantical Modulation for Pedestrian Detection

Despite recent progress, pedestrian detection still suffers from the troublesome problems of small objects, occlusions, and numerous false positives. Intuitively, the rich context information available from urban scenes could help determine the presence and location of pedestrians. For example, roads and sidewalks are good cues for potential pedestrians, while detections on buildings and trees are often false positives. However, most existing pedestrian detectors ignore or inadequately utilize semantic context. In this paper, in order to make full use of the urban-scene semantics to facilitate pedestrian detection, we propose a new method called . First, for efficiency, a semantic prediction module is jointly learned with a baseline detector for semantic predictions. Second, a semantic integration module is designed to exploit the urban-scene semantic context for detection. Specifically, we force it to be an independent detection branch based solely on semantic information. In this way, together with the baseline detector, the fused detection results explicitly depend on both the learned appearance features and the scene context around pedestrians. In addition, while existing methods cannot be applied to the datasets where semantic annotations are not available for training, we introduce a semi-supervised transfer learning approach to make our method suitable for more scenarios. We demonstrate experimentally that, thanks to the integration of semantic context from urban scenes, SMPD can accurately detect small and occluded pedestrians, as well as effectively remove false positives. As a result, SMPD achieves the new state of the art on the Citypersons and Caltech datasets.