关键词: 无重叠视域, 主颜色谱直方图, 相似度测量, 尺度不变特征变换（SIFT）

Abstract: Due to the difference and the separation of the multiple disjoint camera views, the same object’s appearance in different cameras may be very different, which result in matching moving objects across disjoint camera views is a challenging task. This paper proposes an illumination-tolerant appearance representation, which is capable of matching objects in disjoint camera views. This method is based on a first cluster and K-means cluster to abstract object’s major color spectrum histogram, which is introduced to represent a object. Then it uses EMKM to weaken K-means sensitive to the initialization of center values. Finally a feature similarity measurement algorithm is proposed to measure the similarity of any two objects. The paper also introduces SIFT to match objects when can’t tell one from another. Meanwhile, this method can be used in matching objects across overlapping camera views, by means of integrating with other methods to improve the accuracy. Experimental results show the accuracy of the proposed method.

Key words: disjoint camera views, major color spectrum histogram, similarity measurement, Scale Invariant Feature Transform（SIFT）