Unified Keypoint-based Action Recognition Framework via Structured Keypoint Pooling (2303.15270v1)

Abstract: This paper simultaneously addresses three limitations associated with conventional skeleton-based action recognition; skeleton detection and tracking errors, poor variety of the targeted actions, as well as person-wise and frame-wise action recognition. A point cloud deep-learning paradigm is introduced to the action recognition, and a unified framework along with a novel deep neural network architecture called Structured Keypoint Pooling is proposed. The proposed method sparsely aggregates keypoint features in a cascaded manner based on prior knowledge of the data structure (which is inherent in skeletons), such as the instances and frames to which each keypoint belongs, and achieves robustness against input errors. Its less constrained and tracking-free architecture enables time-series keypoints consisting of human skeletons and nonhuman object contours to be efficiently treated as an input 3D point cloud and extends the variety of the targeted action. Furthermore, we propose a Pooling-Switching Trick inspired by Structured Keypoint Pooling. This trick switches the pooling kernels between the training and inference phases to detect person-wise and frame-wise actions in a weakly supervised manner using only video-level action labels. This trick enables our training scheme to naturally introduce novel data augmentation, which mixes multiple point clouds extracted from different videos. In the experiments, we comprehensively verify the effectiveness of the proposed method against the limitations, and the method outperforms state-of-the-art skeleton-based action recognition and spatio-temporal action localization methods.

• The paper introduces Structured Keypoint Pooling, a novel 3D point cloud approach that enhances the robustness of action recognition.
• It employs innovations like the Grouped Pool Block and Pooling-Switching Trick to achieve detailed frame-wise and subject-wise localization.
• Results show superior performance over GCN methods, particularly in handling keypoint detection errors and complex multi-entity interactions.

Overview of "Unified Keypoint-based Action Recognition Framework via Structured Keypoint Pooling"

The paper presents a novel framework for skeleton-based action recognition that addresses several key limitations of existing methods. The authors propose a paradigm shift in skeleton-based action recognition by utilizing a point cloud approach, integrating human and nonhuman keypoints into a deep learning framework termed Structured Keypoint Pooling (SKP). This approach is designed to enhance robustness, increase the diversity of actions recognized, and provide more granular action recognition capabilities.

Methodology

The framework introduced in the paper significantly departs from traditional methods that rely on dense graph structures and graph convolutional networks (GCNs) for modeling spatio-temporal relationships of human skeletons in video sequences. The authors argue that these conventional approaches suffer from various issues:

1. Skeleton Detection and Tracking Errors: Dense graph-based models are highly sensitive to errors in keypoint detection and tracking. Any false positives/negatives or tracking errors can lead to degradation in performance.
2. Limited Scope of Action Categories: Traditional models typically restrict the input to at most two skeletons, rendering them ill-suited for complex interactions involving multiple humans and objects.
3. Inadequate Frame-wise and Person-wise Recognition: Current methods often provide single-label predictions for entire video segments, lacking the ability to localize actions to specific subjects or frames.

To overcome these issues, the proposed Structured Keypoint Pooling framework processes keypoints as a 3D point cloud rather than a sequence of frames, allowing for permutation invariant input and direct modeling of spatial-temporal features. Key innovations include:

• Grouped Pool Block (GPB): This component aggregates features hierarchically by considering the instance level and frame-level relationships of keypoints, addressing the limitations concerning detection and tracking errors.
• Pooling-Switching Trick: A novel technique designed to enhance action localization capabilities. It switches pooling kernels during training and inference, allowing the model to output frame-wise and subject-wise predictions.
• Batch-Mixing Augmentation: Inspired by structured pooling, this data augmentation method mixes point clouds from different videos, broadening the learning context within weakly supervised setups.

Results

The paper includes comprehensive empirical evaluations against state-of-the-art methods across several datasets, such as Kinetics-400, UCF101, and others. The findings outline that:

• The introduction of nonhuman object keypoints (detected using PPNv2) significantly boosts recognition accuracy by expanding the detail and context available in action modeling.
• The proposed framework outperforms conventional GCN approaches, showing robustness against typical skeleton detection errors.
• When tested for weakly supervised spatio-temporal action localization, the method exhibits superior performance with its Pooling-Switching Trick and batch-mixing augmentation, advancing the localization accuracy in complex scenes.

Implications and Future Directions

The proposed framework opens up new avenues for versatile and robust action recognition systems by efficiently integrating diverse keypoint information into a unified model architecture. Its capability to handle keypoint errors and its versatility in recognizing complex actions involving multiple entities suggest substantial practical applications, especially in areas like surveillance and human-computer interactions.

The implications for future research are notable: the paper highlights how point cloud methodologies could impact broader aspects of temporal video analysis and encourages further exploration into multi-modal keypoint incorporation. Additionally, the simplistic yet effective architecture of SKP provides a strong foundation for integrating more sophisticated augmentation and learning techniques that could allow models to learn from even sparser datasets without compromising accuracy.

In conclusion, by addressing the gaps of existing skeleton-based action recognition frameworks via this unified approach, the authors present a compelling case for adopting point cloud paradigms in action recognition, paving the way for future innovations in this space.