基于聚合通道特征的红外行人检测方法
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摘要
基于红外图像的行人检测技术在夜间场景监控、汽车夜间辅助驾驶等相关领域具有重要的作用,然而受红外图像分辨率低、信噪比高等因素影响,当前的很多方法性能不佳。提出了一种基于图像特征通道的红外行人检测算法。利用快速特征金字塔技术在红外图像上进行了滑动窗口检测。实验结果证明,相对于其他常规算法,该算法在实时性和鲁棒性上都有很大的提升。
The pedestrian detection technology based on infrared images has played an important role in night surveillance,night-time driver assistance and other areas.However,because of low resolution and high signal-to-noise ratio,most of the existing infrared pedestrian detection methods have poor performance.An alternative infrared pedestrian detection algorithm based on image feature channels is proposed.In the method,fast feature pyramids are used to implement sliding window detection in infrared images.The results show that compared with other conventional algorithms,this algorithm is improved greatly in realtime performance and robustness.
The pedestrian detection technology based on infrared images has played an important role in night surveillance,night-time driver assistance and other areas.However,because of low resolution and high signal-to-noise ratio,most of the existing infrared pedestrian detection methods have poor performance.An alternative infrared pedestrian detection algorithm based on image feature channels is proposed.In the method,fast feature pyramids are used to implement sliding window detection in infrared images.The results show that compared with other conventional algorithms,this algorithm is improved greatly in realtime performance and robustness.
引文
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[2]O'malley R,Jones E,Glavin M.Detection of Pedestrians in Far-infrared Automotive Night Vision Using Region-growing and Clothing Distortion Compensation[J].Infrared Physics&Technology,2010,53(6):439--449.
[3]Hurney P,Waldron P,Morgan F,et al.Nighttime Pedestrian Classification with Histograms of Oriented Gradients-local Binary Patterns Vectors[J].Iet Intelligent Transport Systems,2015,9(1):75--85.
[4]Baek J,Hong S,Kim J,et al.Efficient Pedestrian Detection at Nighttime Using a Thermal Camera[J].Sensors(Basel,Switzerland),2017,17(8):1850.
[5]Dollár P,Tu Z,Perona P,et al.Integral Channel Features[C].British Machine Vision Conference,2009.
[6]Dollar P,Wojek C,Schiele B,et al.Pedestrian Detection:A Benchmark[C].Computer Vision and Pattern Recognition,2009:304--311.
[7]Dollár P,Appel R,Belongie S,et al.Fast Feature Pyramids for Object Detection[J].IEEE Transactions on Pattern Analysis&Machine Intelligence,2014,36(8):1532--1545.
[8]Hwang S,Park J,Kim N,et al.Multispectral Pedestrian Detection:Benchmark Dataset and Baseline[C].Computer Vision and Pattern Recognition,2015.1037--1045.
[9]Biswas S K,Milanfar P.Linear Support Tensor Machine:Pedestrian Detection in Thermal Infrared Images[J].IEEE Transactions on Image Processing,2016:99.
[10]González A,Fang Z,Socarras Y,et al.Pedestrian Detection at Day/Night Time with Visible and FIR Cameras:A Comparison[J].Sensors,2016,16(6):820.