Special Issue "Enabled and Human-centric Computational Intelligence Solutions for Visual Understanding and Application"

Submission Deadline: 31 March 2022 (closed)
Guest Editors
Prof. Yizhang Jiang, Jiangnan University, China
Prof. Xin Ning, Chinese Academy of Sciences, China
Prof. Weiwei Cai, Northern Arizona University, USA
Dr. Jing Wu, Cardiff University, UK


At present, computer vision (CV) has opened up a new application field. The characteristics of this field are human-centric, that is, human is the main target and service object of the CV system, and it involves the detection, recognition and understanding of static and dynamic features. Specifically, it includes the detection and recognition of various parts of the human body, such as the body, face, and limbs; human actions include gestures, gait, expressions, movements, behaviors, and emotions. human-centric computational intelligence solutions based on CV have the potential to enter a wide range of business, security, education, engineering, entertainment, consumption and daily life.

However, due to a series of complex factors such as illumination, occlusion, forgery attacks, and posture changes in real application scenarios, there are still many challenges and problems in human-centric image understanding that restrict its further application. In recent years, the research progress of deep learning-based CV has shown its potential in practical applications.

Therefore, the purpose of this special issue is to promote practical applications in this field and provide computational intelligence solutions, focusing on the study of computational intelligence methods in human-centric visual understanding and application. This topic will provide new ideas for deep learning-based CV researchers and help solve computational intelligence problems in human-centric visual understanding. Original research and review articles are welcome.

Potential topics include but are not limited to the following:

· Computational Intelligence approaches for human body posture recognition

· Computational Intelligence approaches for behavior recognition

· Computational Intelligence approaches for person re-identification

· CV-enabled and human-centric computational intelligence solutions for industrial applications

· CV-enabled and human-centric computational intelligence solutions for image and video processing

· Human-centric system modeling and design for daily life applications

· Human-centric computational intelligence tools in educational applications

Published Papers

  • Overview of 3D Human Pose Estimation
  • Abstract 3D human pose estimation is a major focus area in the field of computer vision, which plays an important role in practical applications. This article summarizes the framework and research progress related to the estimation of monocular RGB images and videos. An overall perspective of methods integrated with deep learning is introduced. Novel image-based and video-based inputs are proposed as the analysis framework. From this viewpoint, common problems are discussed. The diversity of human postures usually leads to problems such as occlusion and ambiguity, and the lack of training datasets often results in poor generalization ability of the model. Regression… More
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  • Visual Object Tracking via Cascaded RPN Fusion and Coordinate Attention
  • Abstract Recently, Siamese-based trackers have achieved excellent performance in object tracking. However, the high speed and deformation of objects in the movement process make tracking difficult. Therefore, we have incorporated cascaded region-proposal-network (RPN) fusion and coordinate attention into Siamese trackers. The proposed network framework consists of three parts: a feature-extraction sub-network, coordinate attention block, and cascaded RPN block.We exploit the coordinate attention block, which can embed location information into channel attention, to establish long-term spatial location dependence while maintaining channel associations. Thus, the features of different layers are enhanced by the coordinate attention block. We then send these features separately into… More
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  • Underwater Diver Image Enhancement via Dual-Guided Filtering
  • Abstract The scattering and absorption of light propagating underwater cause the underwater images to present low contrast, color deviation, and loss of details, which in turn make human posture recognition challenging. To address these issues, this study introduced the dual-guided filtering technique and developed an underwater diver image improvement method. First, the color distortion of the underwater diver image was solved using white balance technology to obtain a color-corrected image. Second, dual-guided filtering was applied to the white balanced image to correct the distorted color and enhance its details. Four feature weight maps of the two images were then calculated, and… More
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