Adaptive Learning for Target Tracking and True Linking Discovering Across Multiple Non-Overlapping Cameras

To track targets across networked cameras with disjoint views, one of the major problems is to learn the spatio-temporal relationship and the appearance relationship, where the appearance relationship is usually modeled as a brightness transfer function. Traditional methods learning the relationships by using either hand-labeled correspondence or batch-learning procedure are applicable when the environment remains unchanged. However, in many situations such as lighting changes, the environment varies seriously and hence traditional methods fail to work. In this paper, we propose an unsupervised method which learns adaptively and can be applied to long-term monitoring. Furthermore, we propose a method that can avoid weak links and discover the true valid links among the entry/exit zones of cameras from the correspondence. Experimental results demonstrate that our method outperforms existing methods in learning both the spatio-temporal and the appearance relationship, and can achieve high tracking accuracy in both indoor and outdoor environment.