Deep Learning-Based Object Detection and Tracking

Dear Colleagues,

Detection and tracking are fundamental tasks in computer vision with a wide range of applications such as autonomous driving, robotics, video surveillance, intelligent transportation, and augmented reality. These tasks are essential for understanding dynamic environments, identifying and following objects in complex scenes, and enabling intelligent decision-making systems. Detection focuses on identifying the location and class of objects within an image or point cloud, while tracking extends this by estimating the trajectory of objects across time. Together, they form the backbone of various critical applications, including autonomous vehicles, multi-object tracking systems, human activity analysis, and intelligent video analytics.

With the advancement of deep learning techniques, object detection and tracking have made remarkable progress in recent years. However, challenges such as occlusions, real-time processing requirements, multi-object interactions, and the need for robust and scalable models continue to pose significant barriers. Moreover, the integration of detection and tracking into practical systems, especially those involving multimodal data or resource-constrained environments, remains an open problem. To further drive innovation and address these challenges, new algorithms, frameworks, and applications are urgently needed.

This Special Issue aims to bring together recent advances in deep learning-based detection and tracking, with a focus on novel methods, theoretical insights, and impactful applications. Topics of interest include, but are not limited to, the following:

• Deep learning methods for object detection and tracking in 2D and 3D scenes;
• Real-time detection and tracking systems for autonomous driving and robotics;
• Multimodal detection and tracking;
• Deep learning for small object detection and long-term tracking;
• Label-efficient object detection and tracking;
• Lightweight and efficient detection/tracking models for edge devices;
• Tracking and detection in remote sensing, underwater, and aerial imagery;
• Applications in video surveillance, intelligent transportation, and human activity analysis.

We invite researchers and practitioners to submit original research articles, reviews, and case studies showcasing recent breakthroughs and emerging applications in deep learning-based detection and tracking.

Dr. Hao Liu
Dr. Zhuangzi Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• object detection
• multi-object tracking
• 2D images
• 3D point clouds