A Multi-Feature LED Bit Detection Algorithm in Vehicular Optical Camera Communication

In a vehicular optical camera communication (VOCC) system, digital information is transmitted using LED panels and received using cameras. The transmitted bits are obtained by processing the captured images to detect the ON and OFF statuses of LEDs in the array. In determining the LED status, the current LED bit detection algorithms only rely on the grayscale, which is an unreliable feature of LEDs. Consequently, they exhibit poor performance in unfavorable conditions. The contribution of this paper is the proposed multi-feature LED bit detection algorithm that employs three new features of LED: average greyscale ratio (AGR), gradient radial inwardness (GRI), and neighbor greyscale ratio (NGR). Two features, AGR and GRI, individually have substantially more discriminability of LED statuses than greyscale. More importantly, the three proposed features differentiate LED statuses under different perspectives. Consequently, the combination of the three features using Fisher linear discriminant analysis (FLDA) yields outstanding accuracy and robustness of bit detection, even in severe conditions. Highly realistic simulations of a VOCC system are conducted to verify the superiority and robustness of the proposed algorithm.