A novel occlusion index

Abstract

Recovery of 3D human pose and shape under realistic conditions is a challenging task. Despite the recent advances in this field, methods suffer from performance degradation due to naturally occurring occlusions. Benchmark datasets employed to compare the performance of methods under occlusion contain different amounts of challenges. Therefore, assessing the quantitative performance of the benchmarks turns into an equally important task. In this study, we propose a novel metric called the Occlusion Index (OI) to evaluate the severity of occlusion for benchmarks. OI enables the evaluation of the amount of occlusion and partitioning of the benchmark images into subsets according to the severity of the occlusion. We provide occlusion benchmarks obtained from widely-utilized datasets, OCHuman and AGORA, and show that they enable better evaluation of the occlusion robust techniques.