詹智財(cái) 惠浩添 張松松

摘要：針對(duì)尺度不變特征變換(SIFT)算法在匹配時(shí)特征數(shù)量過多導(dǎo)致的耗時(shí)過長(zhǎng)的問題，該文提出一種基于SIFT和主成分分析(PCA)相結(jié)合的SIFT特征降維的視頻車輛跟蹤算法。首先利用SIFT提取出車輛視頻圖像幀中的所有特征點(diǎn)及其特征向量，其次使用PCA算法對(duì)其維數(shù)約減并找出各自的具有代表性的特征參數(shù)，達(dá)到對(duì)特征點(diǎn)向量降維的目的，最后利用歐式距離找出不同車輛圖像幀中相似的車輛。實(shí)驗(yàn)證明，該算法在保證原SIFT算法魯棒性、穩(wěn)定性的同時(shí)減少了計(jì)算量，增加了匹配效率，增強(qiáng)了實(shí)時(shí)性。

關(guān)鍵詞：SIFT；PCA；降維；車輛跟蹤

中圖分類號(hào)：TP391文獻(xiàn)標(biāo)識(shí)碼：A文章編號(hào)：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

在根據(jù)特征值的視頻車輛跟蹤實(shí)現(xiàn)過程中，由于SIFT算法提取視頻圖像特征點(diǎn)個(gè)數(shù)不同，特征點(diǎn)向量維數(shù)較多，而會(huì)使得圖像之間的匹配時(shí)間較長(zhǎng)，該文將PCA引入到SIFT算法中來，利用PCA可將提取數(shù)據(jù)的主成分而忽略次成分達(dá)到不損精度的原理使得SIFT求得的特征描述符的維數(shù)從128維進(jìn)行大幅度降維。通過實(shí)驗(yàn)分析，PCA-SIFT算法達(dá)到了保留SIFT算法的精確性但又解決了匹配時(shí)時(shí)間過長(zhǎng)的問題，因而可以滿足視頻車輛跟蹤的精確性與實(shí)時(shí)性的要求。

該文中PCA-SIFT算法雖減少了大量的匹配時(shí)間，增強(qiáng)了實(shí)時(shí)性，但在要求實(shí)時(shí)很高的場(chǎng)景下本算法不能體現(xiàn)很強(qiáng)的優(yōu)勢(shì)。因此，在今后的研究中，將針對(duì)特征提取算法上進(jìn)一步的研究，使之有SIFT的準(zhǔn)確性，但算法趨于簡(jiǎn)單的方法。

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