Real-time vehicle detection with foreground-based cascade classifier
Real-time vehicle detection with foreground-based cascade classifier
复制标题
使用基于前景的级联分类器进行实时车辆检测
DOI：
10.1049/iet-ipr.2015.0333
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发表时间：
2016-04
影响因子：
2.3
通讯作者：
Wu Qiuxia
中科院分区：
文献类型：
--
作者：
Zhuang Xiaobin;Kang Wenxiong;Wu Qiuxia
基于类Haar特征和级联分类器的车辆检测策略因其有效性和鲁棒性而受到越来越多的关注;然而，这种车辆检测策略依赖于使用不同大小的滑动窗口对整个图像进行穷举扫描，这是繁琐且低效的，因为车辆仅占据整个场景的一小部分。因此，作者提出了一种实时车辆检测算法，它是基于改进的Haar类特征，并结合运动检测与级联的分类器。他们采用视觉背景提取器并辅以形态学处理来获取前景。这些前景保留了车辆特征，并提供了车辆最有可能位于图像中的位置。随后，通过使用分类器的级联而不是单个强分类器，仅在这些位置处执行车辆检测，这能够提高检测性能。作者的算法已经成功地在公共数据集上进行了评估，这证明了它的鲁棒性和实时性。
The strategy based on Haar-like features and the cascade classifier for vehicle detection systems has captured growing attention for its effectiveness and robustness; however, such a vehicle detection strategy relies on exhaustive scanning of an entire image with different sizes sliding windows, which is tedious and inefficient, since a vehicle only occupies a small part of the whole scene. Therefore, the authors propose a real-time vehicle detection algorithm which is based on the improved Haar-like features and combines motion detection with a cascade of classifiers. They adopt a visual background extractor, accompanied by morphological processing, to obtain foregrounds. These foregrounds retain vehicle features and provide the positions within images where vehicles are most likely to be located. Subsequently, vehicle detection is performed only at these positions by using a cascade of classifiers instead of a single strong classifier, which is able to improve the detection performance. The authors' algorithm has been successfully evaluated on the public datasets, which demonstrates its robustness and real-time performance.
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影响因子：
8.2
作者：
Thomas Moranduzzo;F. Melgani
通讯作者：
Thomas Moranduzzo;F. Melgani
DOI：
10.1109/icip.2005.1530126
发表时间：
2005-11
期刊：
IEEE International Conference on Image Processing 2005
影响因子：
--
作者：
L. Tsai;J. Hsieh;Kuo-Chin Fan
通讯作者：
L. Tsai;J. Hsieh;Kuo-Chin Fan
影响因子：
10.6
作者：
L. Tsai;J. Hsieh;Kuo-Chin Fan
通讯作者：
L. Tsai;J. Hsieh;Kuo-Chin Fan
影响因子：
3.3
作者：
Sayanan Sivaraman;Mohan M. Trivedi
通讯作者：
Sayanan Sivaraman;Mohan M. Trivedi
影响因子：
6
作者：
J. Souza;G. Pessin;P. Shinzato;F. Osório;D. Wolf
通讯作者：
J. Souza;G. Pessin;P. Shinzato;F. Osório;D. Wolf