In this paper, we propose a new method for pedestrian detection in thermal image/video by improving the non-maxima suppression(NMS) algorithm. We apply the sliding window detector based deep convolutional neural networks(DNN) to extract pedestrian candidates. A sliding window combined with image pyramids is used to identify pedestrians at varying scales and locations in the image via Convolutional Neural Network(CNN) based binary classification. To improve the performance, we propose an adaptive NMS algorithm to remove false alarms. The proposed NMS uses adaptive overlap-thresholds to overcome the drawback of the standard NMS and improve performance of detection system. It is automatically adjusting the overlap-thresholds based on the density of overlapping windows to improve the accuracy of system. Pedestrian detection experiment results with our thermal database and OSU thermal pedestrian database confirm that the proposed method outperforms the baseline method.