  Pedestrian detection is an initial step to perform outdoor scene analysis,
which plays an essential role in many real-world applications. Although having
enjoyed the merits of deep learning frameworks from the generic object
detectors, pedestrian detection is still a very challenging task due to heavy
occlusion and highly crowded group. Generally, the conventional detectors are
unable to differentiate individuals from each other effectively under such a
dense environment. To tackle this critical problem, we propose an
attribute-aware pedestrian detector to explicitly model people's semantic
attributes in a high-level feature detection fashion. Besides the typical
semantic features, center position, target's scale and offset, we introduce a
pedestrian-oriented attribute feature to encode the high-level semantic
differences among the crowd. Moreover, a novel attribute-feature-based
Non-Maximum Suppression~(NMS) is proposed to distinguish the person from a
highly overlapped group by adaptively rejecting the false-positive results in a
very crowd settings. Furthermore, a novel ground truth target is designed to
alleviate the difficulties caused by the attribute configuration and extremely
class imbalance issues during training. Finally, we evaluate our proposed
attribute-aware pedestrian detector on two benchmark datasets including
CityPersons and CrowdHuman. The experimental results show that our approach
outperforms state-of-the-art methods at a large margin on pedestrian detection.
