计算机科学与应用

Vol.2 No.5 (December 2012)

基于改进的HMAX算法的车型识别应用
Improved HMAX Model for Vehicle Type Recognition

 

作者:

何佳聪 , 蔡恒进 , 邓 娟 , 吕 恒 , 刘翘楚 :武汉大学国际软件学院

 

关键词:

车型识别视觉显著性特征提取HMAX模型Vehicle Type Recognition Visual Saliency Feature Extraction HMAX Model

 

摘要:

本文实现了将改进的HMAX算法应用于车型识别领域。算法的主要创新点是采用ITTI显著性算法选取车辆图片显著点构造图库,通过采用对颜色、亮度和朝向三方面敏感度的综合,构造更具代表性的模板库,提高了算法的识别率。此外,通过计算同一模板与不同图片响应度值的方差,消除了冗余模板,降低了算法识别时间。分析和实验表明,改进的HMAX算法可以有效地实现车型识别,新提出的算法相对原先HMAX模型在识别率可以提高1%~2%,相对当前存在的其他车型识别算法在识别率方面可提升约5%~10%,在保证特征数量的前提下,识别率接近95%;此外,加入模版筛选方法的改进算法相对于原先HMAX模型在识别率基本保持不变的条件下,识别时间可以缩减到原先的1/4,最终根据效益值评估的最优组合中,识别率约为92%,识别时间为0.6 s/幅图片,相对于原先方法均达到了一定的提升。

The paper implements the improved HMAX model for vehicle type (make and model) recognition. The main improvement of this algorithm is using ITTI model with the characteristic of quick response to color, intensity or orien- tation discrimination, to select the saliency area. By this way the template can be more representative and more beneficial to the detection rate. In addition, the calculating of the variance of response degree between different images and the same template eliminates redundant templates, which make contribution to reducing the time of classification. Analyz- ing and experiments claims that the improved HMAX model can be effective and reliable by the detection rate at 95%, with enough features extracted, 5% - 10% higher than current vehicle type recognition methods and 1% - 2% higher than original HMAX model. In addition, with the template screening method added, the improved HMAX model can keep the detection rate and curtail the classification time by quarter time. According to the optimal setting by the final value estimation, the detection rate is about 92%, and the classification time is 0.6 s/image, with promotion to the past.

文章引用:

何佳聪 , 蔡恒进 , 邓 娟 , 吕 恒 , 刘翘楚 (2012) 基于改进的HMAX算法的车型识别应用。 计算机科学与应用, 2, 233-239. doi: 10.12677/CSA.2012.25041

 

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