2D Skeleton Heatmaps and Multi-Modality Fusion for Robust Action Segmentation

Core Concepts

This work proposes a novel 2D skeleton-based action segmentation method that utilizes sequences of 2D skeleton heatmaps as inputs and employs Temporal Convolutional Networks (TCNs) to extract spatiotemporal features. The approach achieves comparable or superior performance and higher robustness against missing keypoints compared to previous 3D skeleton-based methods. Furthermore, the authors explore multi-modality fusion by combining 2D skeleton heatmaps and RGB videos, leading to improved performance.

Abstract

The authors present a 2D skeleton-based action segmentation method that addresses the limitations of previous 3D skeleton-based approaches. The key ideas are:

Transforming 2D skeleton sequences into heatmap sequences, which have regular grid structures suitable for feature extraction using pre-trained CNNs like ResNet/VGG. This allows the use of TCNs to capture spatiotemporal features, in contrast to the GCNs used in 3D skeleton-based methods.

The 2D skeleton heatmap representation is more robust to missing keypoints compared to 3D skeleton coordinates, as each joint/limb is modeled as a Gaussian distribution.

The authors further explore multi-modality fusion by combining 2D skeleton heatmaps and RGB videos, introducing fusion modules at multiple stages of the network for deep supervision. This leads to improved performance over the 2D skeleton-only approach.

Experiments on three action segmentation datasets (UW-IOM, TUM-Kitchen, and Desktop Assembly) demonstrate that the proposed 2D skeleton-based approach achieves comparable or better results than previous 3D skeleton-based and RGB-based methods. The 2D skeleton+RGB fusion approach further boosts the performance, establishing new state-of-the-art results.

Stats

"Our approach achieves similar/better results and higher robustness against missing keypoints than previous 3D skeleton-based methods."
"On UW-IOM, our 2D skeleton+RGB fusion approach achieves 94.54% F1 score, 93.25% Edit distance, 90.12% mAP, and 85.84% Acc."
"On TUM-Kitchen, our 2D skeleton-based approach obtains 81.96% F1 score, 85.14% Edit distance, 58.81% mAP, and 71.55% Acc."
"On Desktop Assembly, our 2D skeleton+RGB fusion approach achieves 98.02% F1 score, 97.75% Edit distance, 91.55% mAP, and 89.40% Acc."

Quotes

"Despite lacking 3D information, our approach yields comparable/superior performances and better robustness against missing keypoints than previous methods on action segmentation datasets."
"To our best knowledge, this is the first work to utilize 2D skeleton heatmap inputs and the first work to explore 2D skeleton+RGB fusion for action segmentation."

Key Insights Distilled From

Action Segmentation Using 2D Skeleton Heatmaps and Multi-Modality Fusion

by Syed Waleed ... at arxiv.org 04-29-2024

https://arxiv.org/pdf/2309.06462.pdf

Deeper Inquiries

How can the proposed 2D skeleton-based approach be extended to handle occlusions and viewpoint changes, where depth cues may be important

To address occlusions and viewpoint changes where depth cues are crucial, the 2D skeleton-based approach can be extended by incorporating additional information or cues. One approach could involve integrating depth estimation techniques to infer depth information from the 2D skeleton heatmaps. By leveraging monocular depth estimation methods or depth prediction models, the system can estimate the depth of each joint in the 2D skeleton, providing valuable depth cues for handling occlusions and viewpoint changes. This depth information can then be fused with the existing 2D skeleton representation to enhance the model's understanding of the spatial relationships between joints in different depth planes. Additionally, incorporating context information from the surrounding environment or objects in the scene can help in inferring occluded joints and improving the robustness of the model against occlusions and viewpoint changes.

What are the potential limitations of the 2D skeleton representation compared to 3D skeletons, and how can they be addressed in future work

The 2D skeleton representation, while effective in capturing human pose information, has certain limitations compared to 3D skeletons. One key limitation is the lack of explicit depth information in 2D skeletons, which can impact the model's ability to handle scenarios where depth cues are essential, such as occlusions and viewpoint changes. To address this limitation, future work could explore methods for inferring depth information from 2D skeletons, either through depth estimation techniques or by incorporating depth sensors in the data capture process. Another limitation is the absence of context details in the skeleton representation, which can be crucial for understanding actions in complex environments. To overcome this limitation, integrating contextual information from the scene or objects can provide valuable cues for improving action segmentation accuracy. Additionally, exploring multi-modal approaches that combine 2D skeleton data with other modalities like RGB images or depth maps can help in capturing richer information for more comprehensive action understanding.

Given the promising results on action segmentation, how can the proposed methods be applied to other related tasks, such as human-robot interaction or ergonomics analysis

The proposed methods for action segmentation using 2D skeleton heatmaps and multi-modality fusion can be applied to various related tasks beyond action segmentation. For human-robot interaction, the models can be adapted to recognize and interpret human actions in real-time, enabling robots to respond effectively to human gestures and commands. By integrating these models into robotic systems, robots can better understand human intentions and interact seamlessly with users. In ergonomics analysis, the methods can be utilized to track and analyze human movements during physical tasks, providing insights into ergonomic risk factors and suggesting improvements to enhance workplace safety and efficiency. By leveraging the capabilities of the proposed approaches in different application domains, such as healthcare, sports analysis, or surveillance, the systems can contribute to a wide range of practical scenarios where human action understanding is essential.