Due to the complexity of real scenes, thermal infrared human object detection faces the following challenges.

 

(1) Restricted by the imaging device, the grayscale of the infrared target is not only affected by its surface temperature, but also related to the surface characteristics, orientation, and radiation wavelength of the target. For a human target, the imaging grayscale is also affected by factors such as the orientation of the human body, the thickness of the clothes, and the material.

 

(2) Thermal infrared images lack color information and also have little texture details. This feature makes it difficult to detect the human body through skin color, clothing, eyes, facial fine features, etc., and it is even more impossible to judge the belonging state when the human objects or between the human body and the environmental objects are adhered or occluded from each other.

 

 

(3) The edges of infrared targets are usually blurred and have halo effects, and there are many interfering targets in the environment, such as lamp posts, animals, vehicles, electrical boxes, buildings, etc. They are easily confused with human targets in infrared imaging.

 

(4) The human body is a typical non-rigid target, and its appearance and posture are complex and changeable, so it is difficult to establish a reasonable model description. Moreover, the movement of the human body is highly random and random, and in most cases does not satisfy the linear or Gaussian motion law, which leads to difficulties in identification and tracking.

 

The solution to the above difficulties relies on breakthroughs in image enhancement, image segmentation, target recognition and classification, and target tracking in the field of computer vision.