K-Track: Kalman Filtering Boosts Deep Point Tracker Performance on Edge Devices

This research explores a novel approach to enhance the efficiency of deep point trackers, a critical component in many AI applications for edge devices. The integration of Kalman filtering shows promise in improving performance and resource utilization in constrained environments.

• •Addresses the challenge of running complex AI models on resource-constrained edge devices.
• •Combines deep learning with Kalman filtering for improved tracking performance.
• •Potentially enables more efficient and real-time AI applications on the edge.