A Benchmark for Temporal Color Constancy
This work addresses a data gap for researchers in computer vision, specifically for temporal color constancy, but it is incremental as it primarily provides a benchmark rather than a novel method.
The authors tackled the lack of realistic large-scale datasets for Temporal Color Constancy by introducing a new benchmark with 600 real-world sequences, a fixed train-test split, and a baseline method that achieves high accuracy, reporting results for over 20 methods.
Temporal Color Constancy (CC) is a recently proposed approach that challenges the conventional single-frame color constancy. The conventional approach is to use a single frame - shot frame - to estimate the scene illumination color. In temporal CC, multiple frames from the view finder sequence are used to estimate the color. However, there are no realistic large scale temporal color constancy datasets for method evaluation. In this work, a new temporal CC benchmark is introduced. The benchmark comprises of (1) 600 real-world sequences recorded with a high-resolution mobile phone camera, (2) a fixed train-test split which ensures consistent evaluation, and (3) a baseline method which achieves high accuracy in the new benchmark and the dataset used in previous works. Results for more than 20 well-known color constancy methods including the recent state-of-the-arts are reported in our experiments.