To adapt the proposed method for use with only a robot-mounted camera while addressing privacy concerns, several strategies can be implemented to maintain high accuracy in human pose estimation:
Optimized Camera Placement: The robot can be designed to dynamically adjust its position and orientation to capture the most informative angles of the human subject. By employing the active measurement framework, the robot can optimize its camera viewpoint to minimize occlusions and truncations, ensuring that the captured images provide sufficient detail for accurate pose estimation.
Sensor Fusion with Non-Visual Cues: The integration of touch and ranging sensors, as proposed, can significantly enhance pose estimation accuracy. By relying on tactile feedback and distance measurements, the system can compensate for the limitations of a single camera, particularly in close proximity scenarios where visual data may be insufficient.
Privacy-Preserving Algorithms: Implementing algorithms that anonymize or obfuscate the captured visual data can help address privacy concerns. For instance, the system could focus on extracting pose information without retaining identifiable features of the individual, ensuring compliance with privacy regulations.
Real-Time Processing and Feedback: By utilizing efficient processing techniques, such as edge computing, the robot can analyze the camera feed in real-time, allowing for immediate adjustments to its pose estimation based on the current context. This can help maintain accuracy even with a limited data set.
User Consent and Interaction: The system can be designed to operate with explicit user consent, where individuals are informed about the use of the robot-mounted camera. This transparency can foster trust and encourage cooperation, leading to better data collection for pose estimation.
By implementing these strategies, the proposed method can effectively utilize a robot-mounted camera for human pose estimation while addressing privacy concerns and maintaining high accuracy in various physical human-robot interaction scenarios.