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This internship is embedded within an active PhD research project aimed at benchmarking State-of-the-Art (SOTA) Multi-Object Tracking (MOT) solutions specifically for Human-Robot Interaction (HRI). In HRI, maintaining a consistent identity for users is critical. Unlike autonomous driving or surveillance, Furhat observe the world from a stationary, eye-level perspective, which presents unique challenges. The core research investigates how different inputs (e.g., Face vs. Full-Body bounding boxes) and detection qualities impact the robot's ability to maintain a social interaction.
Job Responsibility:
Benchmarking SOTA Trackers: You could focus on the evaluation side, running specific trackers (like ByteTrack, OC-SORT, Bot-SORT, …) on our custom dataset to measure their performance in HRI scenarios
Data Collection: You could design and execute new experiments to capture videos of natural Human-Robot Interaction, expanding the diversity of our dataset
Fine-Tuning & Annotation: You could help annotate data to create Ground Truths that allow us to fine-tune trackers for our needs
Requirements:
Master student in Computer Science, Machine Learning, Robotics, or related fields
Strong proficiency in Python
Ideally experience with Annotation Software (e.g., CVAT)
Basic understanding of Object Detection (e.g., YOLO) and Tracking concepts
Eligible to work in Sweden with an active residency