Tracking pedestrian targets over a thermal infrared (TIR) image sequence is a hot topic in visual tracking. The imagery characteristics of TIR targets such as low target-background contrast and far imaging distance make TIR object tracking very difficult. In this paper, based on a convolutional neural network (CNN) and the siamese region proposal network (SiamRPN), we design an improved TIR pedestrian tracker. By fully considering the temporal and spatial information around an object, we firstly construct a CNN-based prediction model to produce the exemplar of a pedestrian target. Then the predicted exemplar is combined with SiamRPN to form an improved real-time TIR pedestrian tracker. The proposed tracker is evaluated on the TIR pedestrian tracking benchmark dataset PTB-TIR. Our experimental results demonstrate that the proposed tracker achieves promising tracking performance. In terms of tracking success rate and precision, our tracker outperforms traditional trackers such as KCF, and state-of-the-art trackers such as SiamRPN, SRDCF, and DSST. Moreover, similar to other siamese-network-based trackers, our tracker runs in real-time.

在热红外（TIR）图像序列上跟踪行人目标是视觉跟踪中的热门话题。TIR目标的成像特性（例如低目标背景对比度和远成像距离）使TIR目标跟踪变得非常困难。本文基于卷积神经网络（CNN）和暹罗区域提议网络（SiamRPN），设计了一种改进的TIR行人跟踪仪。通过充分考虑物体周围的时空信息，我们首先构造了基于CNN的预测模型以产生行人目标的样本。然后将预测的样本与SiamRPN组合以形成改进的实时TIR行人跟踪器。在TIR行人跟踪基准数据集PTB-TIR上评估了建议的跟踪器。我们的实验结果表明，所提出的跟踪器具有良好的跟踪性能。在跟踪成功率和精确度方面，我们的跟踪器优于传统跟踪器（例如KCF）和最先进的跟踪器（例如SiamRPN，SRDCF和DSST）。此外，类似于其他基于暹罗网络的跟踪器，我们的跟踪器是实时运行的。