## 2 Related Work  
Table 1 summarizes the related work in the area using DNN in PD based VLC systems. In all these works, the notion of DNN usage is primarily as an assistive tool to conduct channel assessment under static conditions. While few works have recently started to explore DNNs for tracking the LED signals in camera communication, the emphasis has largely been to use DNN as an auxiliary assistance for the primary traditional signal processing pipeline in the VLC system. Similarly other researchers have used DNNs to detect the region of interest by using DNN first and then applying other techniques to interpret the LED signal. Such a system was experimented by the authors of [11], who used DNNs to find the region of interest in a vehicle for infrastructure settings. Other research that utilizes DL models with VLC systems includes the work of [14], who used autoencoders to mitigate the LED nonlinearity for orthogonal frequency division multiplexing (OFDM)-based VLC systems. The authors performed simulations of adjusting the field of view from 70 degrees to tilts of 5 degrees. Their model is trained to minimize the error in the recovery of the transmitted data. The closest research related to this study was that of [20], where the authors used Convolutional Neural Networks to filter out ambient light from a VLC receiver. Their proposed system showed an improved BER by 40% compared to a system with the same signal-to-noise ratio. The authors of [21] summarized some of the other areas of VLC research where ML/DL models are being incorporated. They described that there are four main applications of ML/DL models in VLC systems, which include non-linearity mitigation, jitter mitigation, modulation discrimination and phase estimation. Models and algorithms such as K-means, Support Vector Machines, logistic regression and DNNs [27; 28; 29] can all be used to perform these tasks. In [30], the authors leverage a 2D CNN for data recovery; however, this was carried out by learning features between color channels and neighboring symbols in the rolling shutter based OCC, \begin{table}
\begin{tabular}{l l l} \hline \hline
**Ref.** & **Year** & **Area of Focus** \\ \hline [PERSON] et al. [12] & 2007 & Using DNN for photodiode (PD) based VLC \\ \hline [PERSON] et al. [13] & 2016 & End–end learning using autoencoders \\ \hline [PERSON] et al. [14] & 2018 & Improving signal detection in quadrature amplitude modulation \\ \hline [PERSON] et al. [15] & 2018 & Using DL for improved binary signal reception \\ \hline [PERSON] et al. [16] & 2018 & Reinforcement learning for a hybrid camera communication system \\ \hline [PERSON] et al. [17; 18; 19] & 2019 & Focused on learning the channel conditions and improve indoor positioning \\ \hline [PERSON] et al. [20] & 2019 & Remove impact of ambient noise \\ \hline [PERSON] et al. [21; 22] & 2019 & Improve receiver demodulation \\ \hline [PERSON] et al. [23] & 2019 & Non-linearity mitigation in OFDM \\ \hline [PERSON] et al. [24] & 2020 & Vehicle–vehicle VLC channel modeling \\ \hline [PERSON] et al. [25] & 2020 & Channel estimation in camera communication \\ \hline [PERSON] et al. [26] & 2020 & Using DL for vehicular camera communication \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Related works in using DNN for VLC systems. which was prone to errors in real time systems with wide variations in lighting conditions. Thus, there is no clear evaluation of whether and how DNNs are suitable in a VLC receiver, particularly for demodulating signals in OCC. The dearth of publicly shared datasets in VLC adds to the challenges in advancing the explorations of using deep learning for VLC. This paper essentially presents an undertaking of investigating the suitability of deep learning classification algorithms for demodulation in OCC and makes a dataset available to the community for further studies in this space. The key challenge in using traditional non-ML computer vision techniques is that these techniques depend on feature extraction from the object's region of interest.