多特征级联的低能见度环境红外行人检测方法

针对低能见度环境中人员的监测和保护问题, 提出了一种基于多特征级联的红外行人检测方法, 利用感兴趣区域长宽比特征和头部Haar特征组成初级分类器, 改进的HOG-SVM完成最终行人识别。所提出的改进的HOG特征提取算法和自适应缩放因子获取算法, 在保证检测率的基础上, 有效地减少了帧间处理时间, 针对目标被遮挡情况, 提出了遮挡情况判断和局部特征识别功能, 由此进一步提高了算法应用于复杂工况下的鲁棒性。实验表明: 该检测方法能够达到91%的检测率, 较现有算法性能得到提升, 同时也满足了系统实时监测要求, 适用于低能见度、粉尘的工况作业环境。

Abstract

Aiming at the problem of personnel monitoring and protection in low visibility environment, an infrared pedestrian detection method based on multi feature association was proposed, the primary classifier was constructed by using the aspect ratio of interest region and the Haar feature of head, and the improved HOG-SVM was used to complete the final pedestrian recognition. An improved HOG feature extraction algorithm and an adaptive scaling factor acquisition algorithm were proposed, and the interframe time was effectively reduced on the basis of guaranteeing the detection accuracy. In view of the occlusion of the target, the occlusion detection and local feature recognition were proposed, which further improved the robustness of the detection system under complicated circumstances. The experimental results show the detection method can achieve the detection rate of 91%, which is better than the existing algorithms, and also meets the real-time monitoring requirements of the system. It is suitable for low visibility and dust working environment.

参考文献

[1] Cui Hao, Li Biao, Shen Zhenkang. Combining shape and texture features for infrared pedestrian detection[C]// Proceedings of the International Conference on multispectral image processing and pattern recognition, 2011: 1-7.

[2] Sun H, Wang C, Wang B, et al. Pyramid binary pattern features for real-time pedestrian detection from infrared videos[J]. Neurocomputing, 2011, 74(5): 797-804.

[3] 刘琼, 王国华, 申旻旻. 基于边缘分割的车载单目远红外行人检测方法[J]. 华南理工大学学报(自然科学版), 2015, 43(1): 87-91, 98.

Liu Qiong, Wang Guohua, Shen Minmin. Pedestrian detection with vehicle-mounted far-infrared monocular sensor based on edge segmentation[J]. Huanan Ligong Daxue Xuebao/Journal of South China University of Technology(Nature Science Edition), 2015, 43(1): 87-91, 98. (in Chinese)

[4] Zheng J, Zhang W, Li B. Pedestrian detection based on background modeling and head-shoulder recognition[C]// International Conference on Wavelet Analysis and Pattern Recognition, 2012:227-232.

[5] Dalal N, Triggs B. Histograms of oriented gradients for human detection[C]//Computer Vision and Pattern Recognition, 2005, IEEE Computer Society Conference on. IEEE, 2005, 1(12): 886-893.

[6] 周红志, 刘祺. 一种改进的梯度直方图远红外行人检测方法[J]. 四川大学学报(自然科学版), 2016, 53(5): 1018-1026.

Zhou Hongzhi, Liu Qi. A far infrared detection recognition description method based on improved gradient histogram [J]. Journal of Sichuan University(Natural Science Edition), 2016, 53(5): 1018-1026. (in Chinese)

[7] Kim D S, Kim M, Kim B S, et al. Histograms of local intensity differences for pedestrian classification in far-infrared images[J]. Electronics Letters, 2013, 49(4): 258-260.

[8] Olmeda D, Escalera A D L, Armingol J M. Far infrared pedestrian detection and tracking for night driving[J]. Robotica, 2011, 29(4): 495-505.

[9] 谢志华, 刘国栋. 基于多尺度局部二元模式共生直方图的红外人脸识别[J]. 红外与激光工程, 2015, 44(1): 391-397.

Xie Zhihua, Liu Guodong. Infrared face recognition based on co-occurrence histogram of multi-scale local binary patterns[J]. Infrared & Laser Engineering, 2015, 44(1): 391-397. (in Chinese)

[10] 夏清, 胡振琪, 位蓓蕾, 等. 一种新的红外热像仪图像边缘检测方法[J]. 红外与激光工程, 2014, 43(1): 318-322.

Xia Qing, Hu Zhenqi, Wei Beilei, et al. New edge detection method for images of infrared thermal imager[J]. Infrared & Laser Engineering, 2014, 43(1): 318-322. (in Chinese)

刘峰, 王思博, 王向军, 赵广伟, 霍文甲. 多特征级联的低能见度环境红外行人检测方法[J]. 红外与激光工程, 2018, 47(6): 0604001. Liu Feng, Wang Sibo, Wang Xiangjun, Zhao GuangWei, Huo Wenjia. Infrared pedestrian detection method in low visibility environment based on multi feature association[J]. Infrared and Laser Engineering, 2018, 47(6): 0604001.

多特征级联的低能见度环境红外行人检测方法

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