北京航空航天大学学报 ›› 2020, Vol. 46 ›› Issue (9): 1721-1729.doi: 10.13700/j.bh.1001-5965.2020.0068

• 论文 • 上一篇    下一篇

热红外视频监控下行人目标前景区域提取

张玉贵, 沈柳青, 胡海苗   

  1. 北京航空航天大学 计算机学院, 北京 100083
  • 收稿日期:2020-03-02 发布日期:2020-09-22
  • 通讯作者: 胡海苗 E-mail:frank0139@163.com
  • 作者简介:张玉贵 男,博士研究生。主要研究方向:图像处理;胡海苗 男,博士,副教授,博士生导师。主要研究方向:图像处理。
  • 基金资助:
    国家重点研发计划(2020AAA0130200);国家自然科学基金(61772058)

Extraction of foreground area of pedestrian objects under thermal infrared video surveillance

ZHANG Yugui, SHEN Liuqing, HU Haimiao   

  1. School of Computer Science and Engineering, Beihang University, Beijing 100083, China
  • Received:2020-03-02 Published:2020-09-22
  • Supported by:
    National Key R & D Program of China (2020AAA0130200); National Natural Science Foundation of China (61772058)

摘要: 在热红外视频监控环境下,针对热红外图像因周围环境温度变化而导致热红外图像灰度值反转的问题,提出了一种通过热红外图像的边界特征和运动特征的融合来提取行人目标前景区域的方法。首先,利用行人目标和周围环境存在的显著性差异来提取行人目标的边界特征,对所提取的边界特征进行边界填充,并利用热红外行人目标分类器来排除误检目标,从而获取最终的边界特征提取结果;其次,利用相邻帧之间的运动信息来获取行人目标的运动特征,对所获取的运动特征进行形态学处理,并利用热红外行人目标分类器来排除误检目标,从而获取最终的运动特征提取结果;最后,对所获取的边界特征提取结果和运动特征提取结果进行融合来获得最终的检测结果。实验证明,在公开的OSU和LSI热红外图像行人目标检测数据集中,所提方法能够有效地降低环境温度变化的不利影响,并提高行人目标前景区域提取的精度。

关键词: 边界特征, 运动特征, 前景区域, 行人目标检测, 灰度值

Abstract: Under the thermal infrared video surveillance, in order to solve the problem of the gray value inversion in thermal infrared image caused by the changes in ambient temperature, this paper proposes a method of extracting the foreground area of pedestrian objects by the fusion of boundary feature and motion feature in the thermal infrared images. First, the boundary feature of the pedestrian objects are extracted by using the significant differences existing between the pedestrian objects and the surrounding environment, and then the extracted boundary feature is subjected to area filling, which is followed by the elimination of the false detection objects by using thermal infrared pedestrian objects classifier, thus obtaining the final boundary feature extraction results. Second, the motion feature of the pedestrian objects are obtained by using the motion information between adjacent frames, and then the resulting motion feature is subjected to the morphological processing, which is followed by the elimination of the false detection objects by using thermal infrared pedestrian objects classifier, thus obtaining the final motion feature extraction results. Finally, the final boundary feature extraction results and the final motion feature extraction results are fused to obtain the final detection results. Our experiments show that the proposed method can effectively reduce the adverse effects brought about by the changes in ambient temperature, and further improve the extraction accuracy of foreground area of the pedestrian objects on the OSU and LSI thermal infrared image pedestrian objects detection dataset.

Key words: boundary feature, motion feature, foreground area, pedestrian objects detection, gray value

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