HOPC: Histogram of Oriented Principal Components of 3D Pointclouds for Action Recognition (1408.3809v4)

Abstract: Existing techniques for 3D action recognition are sensitive to viewpoint variations because they extract features from depth images which change significantly with viewpoint. In contrast, we directly process the pointclouds and propose a new technique for action recognition which is more robust to noise, action speed and viewpoint variations. Our technique consists of a novel descriptor and keypoint detection algorithm. The proposed descriptor is extracted at a point by encoding the Histogram of Oriented Principal Components (HOPC) within an adaptive spatio-temporal support volume around that point. Based on this descriptor, we present a novel method to detect Spatio-Temporal Key-Points (STKPs) in 3D pointcloud sequences. Experimental results show that the proposed descriptor and STKP detector outperform state-of-the-art algorithms on three benchmark human activity datasets. We also introduce a new multiview public dataset and show the robustness of our proposed method to viewpoint variations.

• The paper presents a novel method for 3D pointcloud action recognition using the Histogram of Oriented Principal Components (HOPC) descriptor and a Spatio-Temporal Key-Point (STKP) detection algorithm.
• HOPC captures local geometric characteristics around points using PCA, offering robustness to noise and viewpoint variations compared to traditional depth-image methods.
• Experiments on benchmark datasets show the HOPC method achieves superior accuracy, demonstrating significant improvements in handling viewpoint and motion speed variations for action recognition.

HOPC: Histogram of Oriented Principal Components of 3D Pointclouds for Action Recognition

The paper "HOPC: Histogram of Oriented Principal Components of 3D Pointclouds for Action Recognition" presents a novel method for recognizing human actions by processing 3D pointclouds directly. Unlike traditional methods that rely on depth images, which can be sensitive to noise and viewpoint variations, the proposed approach demonstrates improved robustness in these areas through the direct manipulation of 3D pointcloud data.

Overview and Methodology

The paper introduces a new descriptor, the Histogram of Oriented Principal Components (HOPC), used in conjunction with a novel Spatio-Temporal Key-Point (STKP) detection algorithm. HOPC is developed by performing Principal Component Analysis (PCA) on pointcloud data, which encodes the local geometric characteristics around points. This is done by calculating eigenvectors within an adaptive spatio-temporal support volume and using them to create a histogram representing the pointcloud's orientation and structure.

The key contributions include:

1. HOPC Descriptor: This descriptor captures the local geometric characteristics around each point in the 3D space and is resilient to noise and viewpoint changes due to its orientation-based encoding.
2. STKP Detector: The proposed technique for identifying spatio-temporal keypoints ensures that only significant points contributing to motion are detected, enhancing computational efficiency and accuracy.
3. Speed Normalization Technique: The paper resolves discrepancies in action speeds through an automatic temporal scale selection method that minimizes eigenratios over varying time windows, allowing for a more uniform action recognition performance.

Experimental Results

The authors validate their approach through thorough experimentation on three benchmark datasets: MSRAction3D, MSRGesture3D, and ActionPairs3D. Additionally, they introduce a new Multiview Activity dataset that encapsulates more complex challenges like scale and viewpoint variations. Throughout these experiments, the HOPC descriptor withstood comparison against state-of-the-art techniques, displaying superior performance.

• With respect to MSRAction3D, a standard dataset for action recognition, the HOPC approach achieved an accuracy of up to 86.49%, indicating its competence over competing methods.
• For MSRGesture3D, which lacks full body subjects and includes only hand motions, the proposed technique reached an accuracy of 96.23%, a notable improvement over other methods.
• In the ActionPairs3D dataset, HOPC outperformed other methods by achieving an accuracy of 98.23%, showcasing its effectiveness in distinguishing between actions with high inter-action similarity.
• The UWA3D Multiview dataset confirmed the method's cross-view recognition capabilities, achieving an average accuracy of 82.23% across various side views, significantly outperforming traditional methods, especially in viewpoint-variant scenarios.

Implications and Future Directions

The findings demonstrate considerable improvements in action recognition accuracy across varied viewpoint and motion speed scenarios. These advancements set the ground for deploying more robust human action recognition systems in fields like smart surveillance and high-interaction environments. By extending this research further, there could be potential for integrating HOPC into real-time systems, which may involve addressing challenges related to computational efficiency in real-world deployments. The release of the new multiview dataset also provides a valuable resource for continued research in cross-view action recognition. Future research might focus on refining computational aspects to reduce processing overhead while exploring the integration with other sensory modalities for richer action recognition frameworks.