3DV: 3D Dynamic Voxel for Action Recognition in Depth Video (2005.05501v1)

Abstract: To facilitate depth-based 3D action recognition, 3D dynamic voxel (3DV) is proposed as a novel 3D motion representation. With 3D space voxelization, the key idea of 3DV is to encode 3D motion information within depth video into a regular voxel set (i.e., 3DV) compactly, via temporal rank pooling. Each available 3DV voxel intrinsically involves 3D spatial and motion feature jointly. 3DV is then abstracted as a point set and input into PointNet++ for 3D action recognition, in the end-to-end learning way. The intuition for transferring 3DV into the point set form is that, PointNet++ is lightweight and effective for deep feature learning towards point set. Since 3DV may lose appearance clue, a multi-stream 3D action recognition manner is also proposed to learn motion and appearance feature jointly. To extract richer temporal order information of actions, we also divide the depth video into temporal splits and encode this procedure in 3DV integrally. The extensive experiments on 4 well-established benchmark datasets demonstrate the superiority of our proposition. Impressively, we acquire the accuracy of 82.4% and 93.5% on NTU RGB+D 120 [13] with the cross-subject and crosssetup test setting respectively. 3DV's code is available at https://github.com/3huo/3DV-Action.

• The paper introduces the 3DV representation, which uses temporal rank pooling to condense spatial and motion features from depth videos into a compact voxel set.
• The paper employs PointNet++ to efficiently extract features from the voxelized point set, reducing complexity compared to high-dimensional 3D convolutions.
• The paper integrates a multi-stream approach combining motion and appearance cues, achieving competitive accuracy on benchmarks such as NTU RGB+D 120.

Overview of "3DV: 3D Dynamic Voxel for Action Recognition in Depth Video"

The paper "3DV: 3D Dynamic Voxel for Action Recognition in Depth Video" introduces a novel approach for depth-based 3D action recognition utilizing a 3D dynamic voxel (3DV) as a representation of 3D motion. The proposed 3DV framework encodes motion information from depth videos into a regular voxel set through a process termed temporal rank pooling. This enables each voxel to encompass both 3D spatial and motion features jointly. The 3DV representation is further abstracted into a point set format and processed using PointNet++ for end-to-end deep learning in 3D action recognition tasks.

Key Contributions

1. 3DV Representation: The 3DV representation is a compact form of encoding 3D motion patterns by capturing both temporal and spatial features. Temporal rank pooling is employed on voxelized depth frames to encapsulate the evolution of motion over time, compressing the available data into a single, informative voxel set.
2. Integration with PointNet++: The use of PointNet++, a lightweight and efficient deep learning model, optimizes feature learning from the point set domain. This approach mitigates the typical challenges of training large 3D convolutional networks, offering a more computationally efficient solution.
3. Multi-Stream Framework: To counteract the potential loss of appearance details inherent in the 3DV approach, the authors propose a multi-stream model which incorporates both motion and appearance cues. This facilitates a more robust and comprehensive action recognition by leveraging additional depth frame data in tandem with the 3DV representation.

Experimental Results

The efficacy of the 3DV approach is demonstrated through extensive experiments on multiple benchmark datasets, including NTU RGB+D 120, NTU RGB+D 60, N-UCLA, and UWA3DII. Notable results include achieving 82.4% and 93.5% accuracy on the NTU RGB+D 120 dataset using cross-subject and cross-setup tests, respectively. These outcomes show significant improvement over existing depth-based techniques and are competitive with methods using 3D skeleton data.

Implications and Future Directions

The implications of this research are both practical and theoretical. Practically, the approach presents a scalable and efficient method for 3D action recognition, which is particularly relevant given the large-scale data requirements in modern applications. Theoretically, the work underscores the merit of adopting novel representations like 3DV in conjunction with advanced deep learning networks like PointNet++, which could stimulate further investigation into hybrid spatial and motion-based learning frameworks.

Looking forward, further enhancements could explore optimizing 3DV's discriminative capabilities, particularly for actions involving subtle motion differences. Additionally, the integration of 3DV within multi-modal frameworks which incorporate other data sources, such as RGB video, could enhance performance even further. The continued exploration and refinement of voxel-based representations could facilitate more refined action recognition systems in the implementation of robust and real-time AI solutions.