Single-pedestrian detection aided by two-pedestrian detection
Publication in refereed journal


摘要In this paper, we address the challenging problem of detecting pedestrians who appear in groups. A new approach is proposed for single-pedestrian detection aided by two-pedestrian detection. A mixture model of two-pedestrian detectors is designed to capture the unique visual cues which are formed by nearby pedestrians but cannot be captured by single-pedestrian detectors. A probabilistic framework is proposed to model the relationship between the configurations estimated by single- and two-pedestrian detectors, and to refine the single-pedestrian detection result using two-pedestrian detection. The two-pedestrian detector can integrate with any single-pedestrian detector. Twenty-five state-of-the-art single-pedestrian detection approaches are combined with the two-pedestrian detector on three widely used public datasets: Caltech, TUD-Brussels, and ETH. Experimental results show that our framework improves all these approaches. The average improvement is 9 percent on the Caltech-Test dataset, 11 percent on the TUD-Brussels dataset and 17 percent on the ETH dataset in terms of average miss rate. The lowest average miss rate is reduced from 37 to percent on the Caltech-Test dataset, from 55 to 50 percent on the TUD-Brussels dataset and from 43 to 38 percent on the ETH dataset.
著者Ouyang W., Zeng X., Wang X.
期刊名稱IEEE Transactions on Pattern Analysis and Machine Intelligence
出版社Institute of Electrical and Electronics Engineers
出版地United States
頁次1875 - 1889
關鍵詞contextual information, discriminative model, human detection, object detection, Part based model, pedestrian detection

上次更新時間 2021-27-01 於 01:09