PKU-MMD: A Large Scale Benchmark for Continuous Multi-Modal Human Action Understanding
This provides a standard large-scale benchmark for the computer vision community, particularly benefiting research on action detection, though it is incremental as it builds on existing datasets by adding continuous and multi-modal features.
The authors tackled the lack of large-scale benchmarks for continuous multi-modal 3D human action understanding by introducing PKU-MMD, a dataset with 1076 video sequences, 51 action categories, and 5.4 million frames, and they conducted experiments using various metrics including a new 2D-AP protocol.
Despite the fact that many 3D human activity benchmarks being proposed, most existing action datasets focus on the action recognition tasks for the segmented videos. There is a lack of standard large-scale benchmarks, especially for current popular data-hungry deep learning based methods. In this paper, we introduce a new large scale benchmark (PKU-MMD) for continuous multi-modality 3D human action understanding and cover a wide range of complex human activities with well annotated information. PKU-MMD contains 1076 long video sequences in 51 action categories, performed by 66 subjects in three camera views. It contains almost 20,000 action instances and 5.4 million frames in total. Our dataset also provides multi-modality data sources, including RGB, depth, Infrared Radiation and Skeleton. With different modalities, we conduct extensive experiments on our dataset in terms of two scenarios and evaluate different methods by various metrics, including a new proposed evaluation protocol 2D-AP. We believe this large-scale dataset will benefit future researches on action detection for the community.