Abstract
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.
Highlights
Human vision has the ability to effectively select relevant information out of irrelevant noises and to locate the highly relevant subjects in a scene
Taking the above problems into consideration, our work proposes two unique saliency features from both thermal characteristics and appearance characteristics to describe pedestrians in infrared images
The maximally stable extremal region (MSER) [30] is extracted to measure the stableness of pedestrians, which is further improved by an intensity filter to obtain the thermal analysis based saliency (TAS)
Summary
Human vision has the ability to effectively select relevant information out of irrelevant noises and to locate the highly relevant subjects in a scene. Tree, lamps, and other objects with high intensities may have high values in the contrast map, which may affect the saliency detection of pedestrians To handle these problems, the AAS is introduced which employs the appearance information of pedestrians to enhance the contrast, which is calculated as: SAAS (i) = wi · Con(i),. Different from this, the proposed method propagates saliency scores between the neighboring superpixels (intra-scale neighborhood), and the TAS and AAS feature maps (inter-scale neighborhood). With the use of inter-scale neighborhoods, the wrongly suppressed pedestrian is recovered and highlighted by P1 This result shows the effectiveness of inter-scale neighborhood, which makes TAS and AAS guide each other in the process of saliency propagation to further improve the final saliency.
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