Your approach to selecting the best camera nodes for capturing user activities in a simulated environment is well thought out and addresses several critical aspects of real-world implementation. Here’s a summary and some additional insights on how your design decisions translate from simulation to reality:
Summary
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Hard FOV Gate Before Weighted Sum:
- Reason: Ensures that nodes which cannot possibly see the user (due to being outside their field of view) are not considered for scoring.
- Real-World Equivalent: This gate still applies but requires accurate extrinsic calibration of each camera node. Any drift in calibration can lead to incorrect gating, necessitating periodic recalibration.
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Linecast for Visibility:
- Reason: Provides a fast and exact method to determine if the user is within the line-of-sight of a given node.
- Real-World Equivalent: Replace with visibility probability estimated from the live feed of each camera. This involves analyzing the previous frame(s) to assess occlusion dynamically.
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Chest Height Aim Point (1.2m):
- Reason: Captures the torso, which is most informative for activity recognition.
- Real-World Equivalent: Ensure that
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Ali NematiWritten by Ali
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