## 1 Introduction  
Pedestrian detection has attracted considerable attention from researchers in computer vision. Although many studies in pedestrian detection areas have been reported during the past decade [1; 2; 3; 4; 5; 6; 7; 8], most of them are confined to detecting pedestrians during daytime using visible (VI) cameras. However, the performance of VI cameras depends on good illumination conditions and can be affected when illumination is poor. Recently, multispectral detectors that employ a fusion of infrared (IR) and VI cameras have been developed to achieve robust and reliable pedestrian detection in adverse illumination circumstances [9; 10; 11; 12; 13]. However, the multispectral detectors cannot work well at nighttime since the VI sensor only works when there is a substantial amount of visual information in the environment. Furthermore, the multispectral detectors only support fully aligned images. On a dark night, IR-based pedestrian detectors can effectively replace VI- and multispectral-based detectors, because the IR sensors do not require external light but mainly rely on the radiant temperature of the object. IR pedestrian detection has a wide range of applications, such as patrols, video surveillance, and rescues at night. The main challenges for robust IR pedestrian detection can be classified into two main types. First, IR images have some adverse properties, such as their noisy nature, low-resolution, and no visual detailed information. These adverse properties make the discriminative feature extraction of an object very difficult, and thus affect the detection performance. Second, IR images are susceptible to weather conditions since IR cameras detect the difference in the temperature of the environment. For instance, pedestrians look brighter than the background in cold weather, while their brightness looks similar to the background in hot weather. Traditional IR pedestrian detection methods require manually designed features to describe IR objects, which are not conducive to extracting informative features from unstable IR images since IR sensors are susceptible to changeable weather conditions. In recent years, new technologies have been reported with very promising results. Deep learning has enabled progress on object detection using deep convolutional neural networks (CNNs) due to the capacity to generate semantic features via learning from raw pixels. This approach has superior discriminative ability to recognize targets with various shapes from a complex background [14; 15; 16; 17; 18; 19; 20]. Therefore, it is very natural to apply the CNN to IR pedestrian detection. A CNN-based method is an effective tool for IR object detection since it can handle variations on images affected by environmental changes, as long as these effects are widely present in the dataset. Therefore, our goal is to design an effective CNN framework specialized for IR pedestrian detection at night time that can achieve the best performance regardless of low-resolution or season changes. In this research, a new attention-guided encoder-decoder convolutional neural network for accurate IR pedestrian detection at night, called AED-CNN, is developed. The AED-CNN mainly contains an encoder-decoder module and an attention module. The encoder-decoder structure efficiently captures the long-range context information while keeping the spatial information of IR objects, which is helpful for extracting discriminative features from low-resolution and noisy IR images. Based on the encoder-decoder modules, we further propose an attention module to overcome the variation problem due to changeable thermal energy in IR images under different weather conditions. The following are the main contributions of this work. * First, novel encoder-decoder modules are proposed to generate multi-scale features.