基于度量学习的行人检测算法

针对拥挤场景下行人漏检率较高的问题,设计了新的类平衡策略.其次,采用度量学习方法改进目前的行人语义提取效果,并设计了新的距离度量方法.最后,结合提取的行人语义信息设计了新的非极大值抑制算法.在行人检测数据集CityPersons和CrowdHuman上,与目前的行人检测器进行对比,效果优于目前最优无锚框的行人检测器,同时也证明了度量学习方法在行人检测中的有效性.

Pedestrians detection based on metric learning

Aiming at the problem of high pedestrian missing rate in a crowded, this paper designed a new balance strategy. Secondly, it used metric learning method to improve the effect of pedestrian semantic extraction, and designed a new distance measurement method. Finally, combining with the extracted pedestrian semantic information, it designed a new non-maximum suppression algorithm. Compared with the current pedestrian detector, the proposed detector is better than other pedestrian detectors in Citypersons and Crowdhuman datasets. Experiments also prove the effectiveness of the metric learning method in pedestrian detection.