State-of-the-Art Object Detection, Tracking, and Recognition Techniques

Dear Colleagues,

In the rapidly evolving domain of artificial intelligence (AI) and machine learning (ML), object detection, tracking, and recognition techniques have witnessed remarkable advancements, driving breakthroughs across diverse fields such as computer vision, autonomous driving, and security surveillance. Despite these significant strides, the field continues to grapple with several challenges, including ethical concerns over data privacy, the persistent difficulty of cross-domain object recognition, and the hurdles in achieving real-time performance in complex environments marked by heavy occlusion and rapid motion. This Topic, titled "State-of-the-Art Object Detection, Tracking, and Recognition Techniques", delves into these challenges and more, offering a comprehensive overview of the latest developments in the field. It explores topics such as multi-modal data generation, data privacy, federated learning, cross-domain learning, and large models in object detection, tracking, and recognition. By bringing together researchers from multimedia, computer vision, and machine learning fields of study, this Topic aims to foster collaboration, and share insights, methodologies, and applications related to object detection, tracking, and recognition, ultimately pushing the boundaries of this exciting and dynamic field.

Prof. Dr. Mang Ye
Dr. Jingwen Ye
Dr. Cuiqun Chen
Topic Editors