I. Thermal Infrared Imaging Technology Thermal infrared imaging technology forms images by utilizing the infrared radiation emitted by objects, which is related to the temperature

of the object. Therefore, thermal infrared imaging is unaffected by lighting conditions. In the resulting images, the human body usually appears

brighter than the background due to its relatively high temperature. II. Image Processing Algorithms ## Threshold Segmentation By setting a threshold, the thermal infrared image is converted into a binary image. In this binary image, pixels above the threshold are marked

as the foreground (potential human bodies), while pixels below the threshold are marked as the background. Connected Component Analysis Connected component analysis is performed on the binary image to identify independent connected regions, which may correspond to

pedestrians in the image. To exclude non-human regions, an area threshold can be set, and only connected regions larger than this threshold are retained as candidate

human regions. Motion Analysis In dynamic scenes, moving objects can be detected by analyzing pixel changes across an image sequence. This method improves detection

recall, especially in cases of high ambient temperature or interference from high-temperature objects. By setting a change threshold, the pixel differences at the same position between consecutive frames are compared. If the conditions are

met, the pixel is marked as a "warm" pixel. When the number and area of "warm" pixels satisfy certain criteria, a moving pedestrian is considered

present in that region. Region Fusion and Adjustment Candidate human regions obtained through threshold segmentation and motion analysis are fused to form more complete human regions. The width and height of the fused human regions are adjusted to conform to the actual size proportions of pedestrians. The final human regions are further determined by calculating the width-to-height ratio and area. Algorithm Optimization To improve detection accuracy and efficiency, several optimization algorithms can be adopted. For example: Applying convex-concave grayscale mapping curves to image pixels to enhance image contrast; Using multi-scale feature fusion templates to extract different features from various regions at different image scales; Optimizing feature point extraction through head region constraints and foreground region constraints, among other methods. III. Practical Applications In practical applications, thermal imaging human figure rendering algorithms can be used in various scenarios requiring pedestrian detection.For instance, vehicle-mounted thermal imaging pedestrian detection systems can use this algorithm to accurately detect pedestrians at night

or under severe weather conditions. Security surveillance systems can also apply the algorithm for real-time pedestrian monitoring and tracking

in complex environments.