Traffic accidents, security incidents and crime affairs have a high incidence from evening to early morning due to low illumination. A thermal imaging camera suitable for low illumination environment is installed on the mobile platform to realize the expansion of surveillance area. Pedestrian and background regions in thermal imaging pictures are framed firstly, and then the brightness feature and oriented center symmetric-local binary patterns texture feature are extracted for the training and classification of random ferns classifier. The detected targets are used to extend training sample database, the posterior probability distribution of the classifier is updated, and the online automatic learning of the classifier is realized. Through simulation test, the computing speed of the algorithm for vehicle video is 242.18 s and the false detection rate is 9.53%. For unmanned aerial vehicle video, the computing speed is 14.93 s, and the false detection rate is 4.52%. This algorithm has low false detection rate, fast classification speed and transplant easily. It is suitable for applications with high real-time requirements, and it has certain practical engineering significance.