Adaptive Feature- and Scale-Based Object Tracking with Correlation Filters for Resource-Constrained End Devices in...

Area of Science:

• Computer Vision
• Wireless Communications
• Internet of Things (IoT)

Background:

• Sixth-generation (6G) wireless technology enables rapid Internet of Things (IoT) development, but resource-constrained devices struggle with visual object tracking in large video datasets.
• Discriminative Correlation Filter (DCF)-based trackers offer low computational cost and robustness, yet current methods using fixed features and dense scale intervals are suboptimal.

Purpose of the Study:

• To develop an adaptive mapped-feature and scale-interval method based on DCF to improve visual object tracking for resource-constrained IoT devices.
• To address the suboptimality of fixed feature dimensions and dense scale intervals in existing DCF trackers.

Main Methods:

• Proposed an adaptive mapped-feature response using dimensionality reduction and histogram score maps to integrate multiple features.
• Introduced an adaptive temporal scale estimation method with sparse intervals for improved tracking efficiency.
• Integrated these methods into a DCF-based framework for enhanced visual object tracking.

Main Results:

• The proposed adaptive DCF tracker demonstrated superior performance on benchmark datasets (DTB70, UAV112, UAV123@10fps, UAVDT).
• Achieved a running speed of 41.3 FPS on a low-cost CPU, outperforming state-of-the-art trackers.
• Effectively handles large IoT video data on devices with limited computational resources.

Conclusions:

• The adaptive mapped-feature and scale-interval DCF method significantly improves visual object tracking efficiency and effectiveness.
• This approach is well-suited for real-time applications on resource-constrained devices within 6G-enabled IoT ecosystems.
• Offers a practical solution for visual object tracking challenges in wireless multimedia sensor networks.