Practical Optical Camera Communication Behind Unseen and Complex Backgrounds

Optical camera communication (OCC) holds potential for location-aware data transfer, facilitating applications such as localization and overlaying digital content for mixed reality experiences. However, existing OCC designs commonly require a clean background for reliable demodulation, rendering its use disruptive and impractical. To this end, we propose WinkLink, a novel OCC system capable of robust transmission behind complex backgrounds, even under low signal-to-noise ratio (SNR) conditions. We address the key challenge of extracting subtle signals in the lossy OCC channel by designing a two-stage deep neural network and a context-aware demodulation protocol. The proposed system is trained solely on a synthesized dataset yet generalizes effectively to unseen real-world backgrounds. Through experiments in 12 diverse environments, we demonstrate that WinkLink successfully transmits OCC signals under a low SNR of -20 dB, achieving a substantial 5.8 dB SNR gain. This low SNR translates to an extended distance to 5.5× of baseline (11m with a 10W LED transmitter) and negligible interference on concurrent vision applications. Finally, WinkLink proves its efficacy even when the device is moving, i.e., dynamic backgrounds, making it ready for deployment on mobile devices.