A smart navigation system (an Electronic Travel Aid) based on an object detection mechanism has been designed to detect the presence of obstacles that immediately impede the path, by means of real time video processing. The algorithm can be used for any general purpose navigational aid. This paper is discussed, keeping in mind the navigation of the visually impaired, and is not limited to the same. A video camera feeds images of the surroundings to a Da- Vinci Digital Media Processor, DM642, which works on the video, frame by frame. The processor carries out image processing techniques whose result contains information about the object in terms of image pixels. The algorithm aims to select the object which, among all others, poses maximum threat to the navigation. A database containing a total of three sounds is constructed. Hence, each image translates to a beep, where every beep informs the navigator of the obstacles directly in front of him. This paper implements an algorithm that is more efficient as compared to its predecessors.
Real-Time Multi-Scene Visibility Enhancement for Promoting Navigational Safety of Vessels Under Complex Weather Conditions
School of Navigation, Wuhan University of Technology（武汉理工大学航海学院）；Hong Kong Polytechnic University（香港理工大学）；School of Cyber Science and Technology, Sun Yat-sen University at Shenzhen（中山大学深圳校区网络空间科学与技术学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
AMAVA: Adaptive Motion-Aware Video-to-Audio Framework for Visually-Impaired Assistance
Department of Computer Science, San Francisco State University（计算机科学系，旧金山州立大学）；Department of Mathematics, San Francisco State University（数学系，旧金山州立大学）；Department of Computer Engineering, San Francisco State University（计算机工程系，旧金山州立大学）
Tracking Any Point Methods for Markerless 3D Tissue Tracking in Endoscopic Stereo Images
Institute of Medical Technology and Intelligent Systems, Hamburg University of Technology（医学技术与智能系统研究所，汉堡技术大学）；Department of Otorhinolaryngology, Head and Neck Surgery and Oncology, University Medical Center Hamburg-Eppendorf（耳鼻喉头颈外科与肿瘤学部，汉堡艾本多夫大学医学中心）；Martini-Klinik Prostate Cancer Center, University Medical Center Hamburg-Eppendorf（马尔廷诊所前列腺癌中心，汉堡艾本多夫大学医学中心）；Department of Urology, University Medical Center Hamburg-Eppendorf（泌尿外科，汉堡艾本多夫大学医学中心）
本文提出一种利用2D Tracking Any Point网络结合两个CoTracker模型实现内窥镜立体图像无标记3D组织追踪的新方法，实验验证了其在复杂手术场景中的准确性与潜力。
A Robust Real-Time Computing-based Environment Sensing System for Intelligent Vehicle
Beijing University of Technology（北京工业大学）；Chinese Academy of Sciences（中国科学院）；University of Macau（澳门大学）；Macau University of Science and Technology（澳门科技大学）
Learning Navigation by Visual Localization and Trajectory Prediction
Institute of Mathematics of the Romanian Academy（罗马尼亚科学院数学研究所）；Faculty of Automatic Control and Computers, University Politehnica of Bucharest（布加勒斯特理工大学自动控制与计算机学院）
