詹智财 惠浩添 张松松

摘要：针对尺度不变特征变换(SIFT)算法在匹配时特征数量过多导致的耗时过长的问题，该文提出一种基于SIFT和主成分分析(PCA)相结合的SIFT特征降维的视频车辆跟踪算法。首先利用SIFT提取出车辆视频图像帧中的所有特征点及其特征向量，其次使用PCA算法对其维数约减并找出各自的具有代表性的特征参数，达到对特征点向量降维的目的，最后利用欧式距离找出不同车辆图像帧中相似的车辆。实验证明，该算法在保证原SIFT算法鲁棒性、稳定性的同时减少了计算量，增加了匹配效率，增强了实时性。

关键词：SIFT；PCA；降维；车辆跟踪

中图分类号：TP391文献标识码：A文章编号：1009-3044(2012)16-3954-04

Video Vehicle Tracking Based on Dimension Reduction of SIFT Features

ZHAN Zhi-cai, HUI Hao-tian, ZHANG Song-song

(School of Computer Science and Telecommunication Engineering, JiangsuUniversity, Zhenjiang 212013, China)

Abstract: In this paper, a video vehicle tracking algorithm based on the combination of Scale Invariant Feature Transform (SIFT) and Prin? ciple Component Analysis (PCA), which is called PCA-SIFT, is proposed to deal with the problem that a long time is taken caused by ex? cessive number of characteristics in the matching with SIFT algorithm. Firstly, SIFT is applied to extract all the feature points and vectors of the vehicle video image frames, and then PCA is used to reduce dimensions, followed by the identification of representative characteristic parameters to achieve the purpose of feature dimensionality reduction. Finally, the Euclidean distance is applied to find similar vehicles in the different vehicle image frames. The experimental results show that the algorithm proposed in this paper has advantages of reducing the cost of computation, improving the matching efficiency and enhancing the real-time performance while maintaining the robustness and sta? bility of the original SIFT algorithm.

Key words: Scale Invariant Feature Transform; Principle Component Analysis; dimensionality reduction; vehicle tracking

在根据特征值的视频车辆跟踪实现过程中，由于SIFT算法提取视频图像特征点个数不同，特征点向量维数较多，而会使得图像之间的匹配时间较长，该文将PCA引入到SIFT算法中来，利用PCA可将提取数据的主成分而忽略次成分达到不损精度的原理使得SIFT求得的特征描述符的维数从128维进行大幅度降维。通过实验分析，PCA-SIFT算法达到了保留SIFT算法的精确性但又解决了匹配时时间过长的问题，因而可以满足视频车辆跟踪的精确性与实时性的要求。

该文中PCA-SIFT算法虽减少了大量的匹配时间，增强了实时性，但在要求实时很高的场景下本算法不能体现很强的优势。因此，在今后的研究中，将针对特征提取算法上进一步的研究，使之有SIFT的准确性，但算法趋于简单的方法。

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