Mutual Guidance-Based Saliency Propagation for Infrared Pedestrian Images

Saliency detection is important in computer vision. However, most of the existing saliency models are designed for visible images. It is still a challenging problem to apply saliency detection algorithms on infrared images. In this paper, an effective propagation based saliency detection method for infrared pedestrian images is proposed. Firstly, based on the thermal characteristics of infrared images and thermal radiation models, a thermal analysis based saliency (TAS) is introduced. TAS measures the stableness of pedestrians based on maximally stable extremal regions, which is further improved by an intensity filter. Then, by taking into account the appearance characteristic of pedestrians, an appearance analysis weighted saliency (AAS) is proposed which combines the intensity and shape features of pedestrians to improve the intensity contrast. Finally, besides the commonly used intra-scale neighborhood, an inter-scale neighborhood is introduced to jointly construct a mutual guidance-based saliency propagation model. This model could simultaneously integrate the saliency features and improve the saliency performance. Two datasets DIP and IMS with 600 infrared pedestrian images are published. Then, extensive experiments and comparisons with state-of-the-art methods demonstrate the effectiveness of the proposed saliency method for infrared pedestrian images.