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Matching tracks from a single individual across disjoint camera views is a challenging task in video surveillance. In this paper, a major color spectrum histogram representation (MCSHR) is introduced to represent a moving object by using a normalized distance between two points in the RGB space. Then, an incremental MCSHR is proposed to cope with small pose changes and segmentation errors occurring along the track. Finally, a similarity measurement algorithm is proposed based on the incremental MCSHR to measure the similarity of any two tracked moving objects. The proposed similarity measurement algorithm proved capable of measuring the similarity of the two moving objects accurately. Experimental results show that with three to five frames integration, the proposed incremental MCSHR algorithm can make matching more robust and reliable than single-frame matching, especially for small pose changes. The matching performance is not obviously improved instead when the number of integration is more than five. The similarity of a same moving object in two different tracks has been improved from 92% to 95% with the integration number increased from three to five, while two different moving objects have been easily discriminated. The proposed algorithm can be used to match tracks from single individuals in camera networks, which do not provide full coverage of the monitored space.