Object tracking across non-overlapping views by learning inter-camera transfer models

In this paper, we introduce a novel algorithm to solve the problem of object tracking across multiple non-overlapping cameras by learning inter-camera transfer models. The transfer models are divided into two parts according to different kinds of cues, i.e. spatio-temporal cues and appearance cues. To learn spatio-temporal transfer models across cameras, an unsupervised topology recovering approach based on N-neighbor accumulated cross-correlations is proposed, which estimates the topology of a non-overlapping multi-camera network. Different from previous methods, the proposed topology recovering method can deal with large amounts of data without considering the size of time window. To learn inter-camera appearance transfer models, a color transfer method is used to model the changes of color characteristics across cameras, which has an advantage of low requirements to training samples, making update efficient when illumination conditions change. The experiments are performed on different datasets. Experimental results demonstrate the effectiveness of the proposed algorithm.