Exploring Event-based Human Pose Estimation with 3D Event Representations (2311.04591v4)

Abstract: Human pose estimation is a fundamental and appealing task in computer vision. Although traditional cameras are commonly applied, their reliability decreases in scenarios under high dynamic range or heavy motion blur, where event cameras offer a robust solution. Predominant event-based methods accumulate events into frames, ignoring the asynchronous and high temporal resolution that is crucial for distinguishing distinct actions. To address this issue and to unlock the 3D potential of event information, we introduce two 3D event representations: the Rasterized Event Point Cloud (RasEPC) and the Decoupled Event Voxel (DEV). The RasEPC aggregates events within concise temporal slices at identical positions, preserving their 3D attributes along with statistical information, thereby significantly reducing memory and computational demands. Meanwhile, the DEV representation discretizes events into voxels and projects them across three orthogonal planes, utilizing decoupled event attention to retrieve 3D cues from the 2D planes. Furthermore, we develop and release EV-3DPW, a synthetic event-based dataset crafted to facilitate training and quantitative analysis in outdoor scenes. Our methods are tested on the DHP19 public dataset, MMHPSD dataset, and our EV-3DPW dataset, with further qualitative validation via a derived driving scene dataset EV-JAAD and an outdoor collection vehicle. Our code and dataset have been made publicly available at https://github.com/MasterHow/EventPointPose.

• The paper introduces RasEPC and DEV methods to leverage event cameras' high temporal resolution for efficient human pose estimation.
• It demonstrates that the DEV approach reduces Mean Per Joint Position Error by up to 18.25%, outperforming traditional methods.
• The techniques enable real-time processing on edge devices and offer a promising framework for future event-based vision applications.

An Examination of 3D Event Representations for Human Pose Estimation

The research paper "Rethinking Event-based Human Pose Estimation with 3D Event Representations" by Xiaoting Yin et al. offers a detailed investigation into the design and utilization of novel 3D event representations for the task of Human Pose Estimation (HPE) using event cameras. Event-based sensors provide an advantageous approach over traditional RGB cameras, especially in scenarios with high dynamic ranges and motion blur. This paper challenges the prevalent practice of converting asynchronous event data into frame-like structures, which compromises the intrinsic properties of event streams.

Key Contributions and Methodologies

The authors introduce two core representations: the Rasterized Event Point Cloud (RasEPC) and the Decoupled Event Voxel (DEV). The RasEPC method capitalizes on the event cameras' high temporal resolution by aggregating events into three dimensions while effectively minimizing computational demands. It adapts LiDAR point cloud methods for event camera data, demonstrating compatibility with known point cloud network backbones such as PointNet, DGCNN, and Point Transformer.

On the other hand, the DEV representation innovatively projects the 3D event space onto three orthogonal 2D planes, allowing the integration of standard convolutional networks. Additionally, the incorporation of a Decoupled Event Attention (DEA) mechanism enhances the ability to extract spatial-temporal features from these projections, effectively embedding 3D contextual information into 2D representations.

To support the development and validation of these methods, the paper introduces a new synthetic dataset, EV-3DPW, created using an event simulator on existing RGB datasets, thus enabling the exploration of event-based HPE in outdoor conditions.

Experimental Evaluation and Results

The experiments conducted on both the DHP19 dataset and the newly constructed EV-3DPW dataset demonstrate considerable improvements in accuracy and efficiency. Numerically, the DEV method outperformed traditional methods, achieving a reduction in Mean Per Joint Position Error (MPJPE) by up to 18.25% on the DHP19 dataset. This underscores the efficacy of maintaining temporal dimension information for HPE tasks. Moreover, the RasEPC approach showcases real-time processing capabilities on edge-computing platforms, indicating its potential for mobile applications.

Implications and Future Directions

The implications of this research are significant for both the practical and theoretical aspects of computer vision and AI. Practically, the reduced memory and computational requirements of these methods make them suitable for real-time applications on mobile and edge devices, addressing current limitations in deploying HPE in dynamic environments. Theoretically, these representations advocate for a shift from traditional frame-based event processing to methods that retain the inherent qualities of event data.

The potential of these representations extends beyond pose estimation to other areas of dynamic vision sensor applications, such as action recognition and shape inference. Future advancements may focus on optimizing these methods for multi-person scenarios or exploring end-to-end event-based cognitive models, enhancing the applicability of event cameras in complex, real-world environments.

Overall, this paper provides a solid framework for advancing event-based HPE, urging the research community to consider novel dimensional representations of event data to enhance performance and efficiency.