Chen, K.-W.K.-W.ChenYI-PING HUNG2020-06-112020-06-11201010514651https://scholars.lib.ntu.edu.tw/handle/123456789/500527https://www.scopus.com/inward/record.uri?eid=2-s2.0-78149481241&doi=10.1109%2fICPR.2010.44&partnerID=40&md5=ecc80a3f33b8914bfe20fb6200675855For target tracking across multiple cameras with disjoint views, previous works usually employed multiple cues and focused on learning a better matching model of each cue, separately. However, none of them had discussed how to integrate these cues to improve performance, to our best knowledge. In this paper, we look into the multi-cue integration problem and propose an unsupervised learning method since a complicated training phase is not always viable. In the experiments, we evaluate several types of score fusion methods and show that our approach learns well and can be applied to large camera networks more easily. © 2010 IEEE.Camera network; Cue integration; Disjoint views; Matching models; Multi-camera tracking; Multiple cameras; Score fusion; Training phase; Unsupervised learning method; Pattern recognition; Target tracking; Unsupervised learning; Video cameras; Camerasconference paper10.1109/ICPR.2010.442-s2.0-78149481241