Transitioning from feature-based to vision-based approaches in real-world applications may pose several challenges. One significant challenge is the accurate extraction and interpretation of relevant information from visual data captured by cameras. Vision-based approaches rely on raw images as input, which may contain noise or variations due to lighting conditions or occlusions. Ensuring robustness against these factors while extracting meaningful features for TSC algorithms is crucial but challenging.
Another challenge is related to scalability and computational efficiency. Processing high-dimensional visual data requires substantial computational resources compared to traditional feature-based methods that use pre-defined features extracted from sensors like loop detectors or pressure plates. Balancing accuracy with computational efficiency becomes essential when deploying vision-based TSC systems in real-time applications.
Furthermore, ensuring interpretability and explainability of decisions made by vision-based TSC models poses a challenge. Unlike feature-based methods where engineers have clear insights into how decisions are made based on specific features like queue length or vehicle density, interpreting decisions made by deep learning models trained on visual data can be complex due to their black-box nature.