|
|
论文:2017,Vol:35,Issue(2):274-279 |
|
|
引用本文： |
|
|
曲仕茹, 李桃. 基于改进CoHOG-LQC的行人检测算法[J]. 西北工业大学学报 |
|
|
Qu Shiru, Li Tao. Extended CoHOG-LQC for Pedestrian Detection[J]. Northwestern polytechnical university |
|
|
|
|
|
|
|
| 基于改进CoHOG-LQC的行人检测算法 |
|
| 曲仕茹, 李桃 |
|
| 西北工业大学 自动化学院, 陕西 西安 710129 |
| 摘要: |
| 针对行人检测过程中，易对相似目标产生误判的问题，并结合局部纹理特征描述子对图像边缘、方向信息的描述能力与检测精度的强相关性，同时考虑到基于LBP和HOG的特征融合方法存在结构利用率低、光谱信息损失多的缺点，提出了一种基于LQC和CoHOG特征融合的行人检测算法。首先通过LQC算子提取图像的纹理谱特征，同时使用积分图计算CoHOG特征值，以提取原始图像的边缘特征及基于LQC 特征谱的CoHOG特征。然后将上述特征与CoHOG边缘特征融合，得到融合特征描述图像，最后使用HIKSVM分类器实现输入图像的检测与识别。为验证算法的有效性，分别在MIT行人数据库、Caltech行人数据库和INRIA行人数据库上进行实验。实验结果表明，提出的方法可以有效提高行人检测精度和效率。 |
| 关键词:
行人检测
共生梯度方向直方图
局部量化编码
特征提取
特征融合
|
|
| Extended CoHOG-LQC for Pedestrian Detection |
|
| Qu Shiru, Li Tao |
|
| School of Automation, Northwestern Polytechnical University, Xi'an 710072, China |
| Abstract: |
| In accordance with the miscalculation over the recognition of resemble objects in the process of pedestrian detection, and strong correlations between detection precision and description capability that local texture feature descriptors can achieve when acquiring the characteristics of image edge and direction, considering the defects that the low space efficiency as well as high spectral information loss of the pedestrian tracking algorithm which based on fusion among Local Binary Pattern (LBP) and Histograms of Oriented Gradient (HOG). We proposed a novel algorithm based on the fusion among Local Quantization Code (LQC) feature and Co-Occurrence Histogram Oriented Gradient (CoHOG) feature for detecting passenger. Firstly, the spectral property of the image were extracted efficiently using LQC feature descriptor from image. Next, the calculation using integral image was established to withdraw edge characteristic and CoHOG features based on LQC character spectrums from the original image. For further procedure, the CoHOG edge feature are fused with them, then the fusion feature image is acquired. At last, Histogram Intersection Kernel Support Vector Machine (HIKSVM) classifiers were performed for detection and recognition. To validate the effectiveness of the algorithm, experiments are carried out on 3 public pedestrian dataset including MIT, Caltech and INRIA. The results demonstrates that the method is effective to raise accuracy and efficiency of clustering process. |
| Key words:
pedestrian detection
CoHOG
LQC
feature extraction
feature fusion
image fusion
pixels
support vector machines
|
|
| 收稿日期: 2016-10-17
修回日期:
|
| DOI: |
| 通讯作者:
Email： |
| 作者简介: 曲仕茹(1963-),女,西北工业大学教授,主要从事图像处理与分析、交通信息与控制的研究。
|
|
| 相关功能 |
|
|
|
|
| 作者相关文章 |
|
| 曲仕茹 在本刊中的所有文章 |
| 李桃 在本刊中的所有文章 |
|
|
|
|
|
|
|
|
参考文献: |
|
|
[1] Dalal N, Triggs B. Histograms of Oriented Gradients for Human Detection[C]//2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE Computer Society, 2005:886-893
[2] Dollar P, Wojek C, Schiele B, et al. Pedestrian Detection:An Evaluation of the State of the Art[J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 2012, 34(4):743-761
[3] Tang S, Andriluka M, Schiele B. Detection and Tracking of Occluded People[J]. International Journal of Computer Vision, 2014, 110(1):58-69
[4] Wang X, Han T X, Yan S. An HOG-LBP Human Detector with Partial Occlusion Handling[C]//2009 IEEE 12th International Conference on Computer Vision, 2009:32-39
[5] Ouyang W, Wang X. Joint Deep Learning for Pedestrian Detection[C]//Proceedings of the IEEE International Conference on Computer Vision, 2013:2056-2063
[6] Walk S, Majer N, Schindler K, et al. New Features and Insights for Pedestrian Detection[C]//2010 IEEE Conference on Computer Vision and Pattern Recognition, 2010:1030-1037
[7] Ojala T, Harwood T. A Comparative Study of Texture Measures with Classification Based on Featured Distributions[J]. Pattern Recognition, 1996, 29(1):51-59
[8] Watanabe T, Ito S, Yokoi K. Co-Occurrence Histograms of Oriented Gradients for Pedestrian Detection[C]//Pacific Rim Symposium on Advances in Image and Video Technology, 2009:37-47
[9] Zhao Y, Wang R G, Wang W M, et al. Local Quantization Code Histogram for Texture Classification[J]. Neurocomputing, 2016, 207:354-364
[10] Alpaslan N, Turhan M M, Hanbay D. Determining Noise Performance of Co-Occurrence GMuLBP on Object Detection Task[C]//Sixth International Conference on Machine Vision, 2013:90671Y-90671Y-5 |
|
|
|
|
|
|
|
