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.