Applications of Object Tracking in Computer Vision

Dear Colleagues,

Overview:

Object tracking, a core task in computer vision, underpins critical real-world applications—enabling autonomous vehicles to navigate traffic safely, ensuring public safety in monitoring crowded events, and facilitating precise work in unmanned aerial vehicles by tracking targets. Despite notable progress, its practical deployment is hindered by scenario-specific challenges that directly impact real-world utility.

Applications in dynamic occlusion conditions: Existing object tracking methods often struggle with complex occlusion scenarios involving multiple moving objects or long-term occlusion by static obstacles. For instance, in unmanned aerial vehicles, drastic changes in appearance caused by frequent viewpoint and scale changes can disrupt tracking, risking procedural errors.

Application deployment in real-time edge devices: Many applications rely on resource-constrained edge devices—such as drones for search-and-rescue missions and smart traffic cameras. Existing object tracking methods, particularly transformer-based models, demand substantial computational resources for feature extraction and temporal reasoning, making them incompatible with edge devices with limited power and memory.

Performance degradation in open-world applications: Existing object tracking methods are often vulnerable to conditions of the open world, such as varying lighting conditions, occlusions, and weather changes. These challenges can lead to significant performance degradation in real-world applications, particularly in safety-critical domains such as autonomous driving.

The insufficient tracking adaptability of applications in unseen objects: Conventional object tracking methods assume that test data comes from a known set of classes. In dynamic environments, new objects constantly appear. Existing object tracking applications cannot “recognize the unknown” and may misclassify them as known categories.

This Special Issue aims to bridge these gaps by fostering innovations that enhance the practical utility of object tracking in real-world applications.

Aims & Scope

This Special Issue seeks innovative research papers that address these challenges and propose novel techniques for applications of object tracking in computer vision. Potential topics of interest include, but are not limited to, the following:

Multi-object tracking applications in unmanned aerial vehicles: The use of techniques to handle the unstable motion patterns of the unmanned aerial vehicle itself and the failure of target tracking induced by drastic changes in the appearance of the tracking target.

Object tracking applications in edge device: Developing new object tracking techniques for federated learning, continual learning, and multimodal learning in edge devices.

New benchmarks in real-world applications: The development of new benchmarks for assessing the performance of object tracking in real-world applications, ensuring that they are robust, interpretable, and fair.

Robustness object tracking: Techniques used to handle partial/total occlusion, camouflage detection, and environmental variability.

Open-world object tracking: Techniques for enabling the adaption of object tracking tasks to new classes and domains, ensuring that they remain effective in dynamic environments.

Vision foundation models in object tracking: Methods implemented to explore the use of large vision language models or vision foundation models for object tracking tasks.

Dr. Shishun Tian
Dr. Muxin Liao
Guest Editors

Manuscript Submission Information

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• object tracking applications
• unmanned aerial vehicles
• open-world applications
• datasets and benchmarks
• vision foundation models