I’m seeking guidance on implementing a multi-sensor voting process for enhanced scene understanding. Specifically, I’m looking to integrate data from RGB cameras, radar, and audio sensors to overcome the limitations of video-only analysis.
My difficulty lies in designing an effective voting mechanism between different learning modules, each dedicated to processing a specific sensor input. The goal is to achieve more robust and comprehensive scene interpretation. I’d greatly appreciate insights from experts in multi-modal sensor fusion or ensemble learning techniques applied to scene understanding.
Any practical advice on architecting such a system would be invaluable.
I once worked on industrial environmental problem detection systems. These combined inputs from physical, chemical, vibration, and magnetic sensors. The key thing we learned was to account for hysteresis, which is the delay between changing input conditions and sensor output. The integration (or combination, or voting) worked better when we allowed for this effect to fully mature in the sensor before polling or measuring it. In a sense (pardon the pun), there is some low-level ‘intelligence’ within the sensor itself.
This is a significant difference between mechanical (and biological) systems versus electronic (and computational) ones.