1. 毕业设计（论文）的内容和要求

车辆身份识别系统是智能交通的重要分支，它需要人工智能、图像处理、计算机视觉、模式识别等相关技术的综合应用。

目前国内的车牌识别技术已经日益成熟，随着智能交通技术应用的不断加深，工业界迫切希望提取更多元的车辆信息，除车牌号码外，还需要车辆的厂牌、型号以及颜色等信息特征。

这些特征在停车场无人管理、交通事故处理、交通肇事逃逸、违章车辆自动记录等领域具有广泛而迫切的应用需求。

2. 参考文献

[1]. Sullivan, G.D., et al., Model-based vehicle detection and classification using orthographic approximations. Image and Vision Computing, 1997. 15(8): p. 649-654. [2]. Tan, T., G.D. Sullivan and K.D. Baker, Model-based localisation and recognition of road vehicles. International Journal of Computer Vision, 1998. 27(1): p. 5-25. [3]. Gupte, S., et al., Detection and classification of vehicles. Intelligent Transportation Systems, IEEE Transactions on, 2002. 3(1): p. 37-47. [4]. Ji, P., L. Jin and X. Li. Vision-based vehicle type classification using partial gabor filter bank. 2007: IEEE. [5]. Morris, B.T. and M.M. Trivedi, Learning, modeling, and classification of vehicle track patterns from live video. Intelligent Transportation Systems, IEEE Transactions on, 2008. 9(3): p. 425-437. [6]. Zhang, L., et al. Real-time object classification in video surveillance basedon appearance learning. 2007: IEEE. [7]. 王枚等, 基于小波变换和不变矩的车标识别方法. 海军航空工程学院学报, 2007. 22(6): 第655-6508页. [8]. 王枚等, 基于 PCA 与不变矩的车标定位与识别. 武汉大学学报: 信息科学版, 2008. 33(1): 第36-40页. [9]. Petrovic, V.S. and T.F. Cootes. Analysis of Features for Rigid Structure Vehicle Type Recognition. 2004. [10]. Munroe, D.T. and M.G. Madden, Multi-class and single-class classification approaches to vehicle model recognition from images. Proc. AICS, 2005. . [11]. Negri, P., et al. An oriented-contour point based voting algorithm for vehicle type classification. 2006: IEEE. [12]. Hinton, G.E. and R.R. Salakhutdinov, Reducing the dimensionality of data with neural networks. Science, 2006. 313(5786): p. 504-507. [13]. Lowe, D.G. Object recognition from local scale-invariant features. 1999: Ieee. [14]. Lindeberg, T., Scale-space theory: A basic tool for analyzing structures at different scales. Journal of applied statistics, 1994. 21(1-2): p. 225-270. [15]. Mikolajczyk, K. and C. Schmid, Scale affine invariant interest point detectors. International journal of computer vision, 2004. 60(1): p. 63-86. [16]. Ke, Y. and R. Sukthankar. PCA-SIFT: A more distinctive representation for local image descriptors. 2004: IEEE. [17]. Bay, H., T. Tuytelaars and L. Van Gool, Surf: Speeded up robust features. 2006, Springer. p. 404-417. [18]. Wang, X., et al. Contextual weighting for vocabulary tree based image retrieval. 2011: IEEE. [19]. Nister, D. and H. Stewenius. Scalable recognition with a vocabulary tree. 2006: IEEE. 2002. 31(1): 第25-29页. [20]. Coates, A., A.Y. Ng and H. Lee. An analysis of single-layer networks in unsupervised feature learning. 2011. [21]. Boureau, Y., J. Ponce and Y. LeCun. A theoretical analysis of feature pooling in visual recognition. 2010. [22]. Coates, A., et al. Text detection and character recognition in scene images with unsupervised feature learning. 2011: IEEE. [23]. Coates, A., et al. Text detection and character recognition in scene images with unsupervised feature learning. 2011: IEEE. [24]. Wang, T., et al. End-to-end text recognition with convolutional neural networks. 2012: IEEE. [25].Coates, A. and A.Y. Ng, Learning feature representations with k-means. 2012, Springer. p. 561-580.

3. 毕业设计（论文）进程安排

12-24～01-18 查阅文献、外文翻译、开题报告、熟悉图纸 01-17开题 02-25～03-10 确定系统总体方案 3月11日～3月21日 确定算法、细化算法 3月22日～4月29日 程序编写、调试、论文纲要 中期检查 4月30日～5月15日 完善程序、提交论文提纲 5月16日～6月8日 撰写毕业论文初稿、终稿 6月9日～6月11日 提交所有毕业设计正式材料电子稿与打印稿 6月12日之前 准备答辩 6月12日～6月14日 答辩