关键词: 外观特征, 自适应类数, 峰值轮廓, 候选峰, 残余峰能量

Abstract: To cluster the appearance feature of the moving object plays an important role in object modeling, detecting and tracking applications. However, it is difficult to pre-set the cluster number for random data samples. In this paper, a novel framework of online determining the cluster number is proposed aiming at adaptively classifying the appearance features of vehicles. Iterative threshold values and RGB component detection fusion are utilized to automatically extract the object region from the background and restrain the background disturbance. The peak contour of the V（Value） color component histogram（under HSV space） in the object region is extracted. The sequential grayscale differences of adjacent candidate peaks and the residual peak energy are calculated and used to derive the cluster number. The sample intensity matrix of the object appearance is constructed with S（Saturation） and V（Value） components, and clustered with the obtained cluster number and K-means algorithm. Results from experiments show the excellent performance of the proposed algorithm in the class number adaptivity, the clustering effectiveness and the computational efficiency.

Key words: appearance feature, adaptive cluster number, peak contour, candidate peak, residual peak energy