Fast and Robust Multi-Person 3D Pose Estimation from Multiple Views (1901.04111v1)

Abstract: This paper addresses the problem of 3D pose estimation for multiple people in a few calibrated camera views. The main challenge of this problem is to find the cross-view correspondences among noisy and incomplete 2D pose predictions. Most previous methods address this challenge by directly reasoning in 3D using a pictorial structure model, which is inefficient due to the huge state space. We propose a fast and robust approach to solve this problem. Our key idea is to use a multi-way matching algorithm to cluster the detected 2D poses in all views. Each resulting cluster encodes 2D poses of the same person across different views and consistent correspondences across the keypoints, from which the 3D pose of each person can be effectively inferred. The proposed convex optimization based multi-way matching algorithm is efficient and robust against missing and false detections, without knowing the number of people in the scene. Moreover, we propose to combine geometric and appearance cues for cross-view matching. The proposed approach achieves significant performance gains from the state-of-the-art (96.3% vs. 90.6% and 96.9% vs. 88% on the Campus and Shelf datasets, respectively), while being efficient for real-time applications.

An Overview of Fast and Robust Multi-Person 3D Pose Estimation from Multiple Views

The paper "Fast and Robust Multi-Person 3D Pose Estimation from Multiple Views" presents an innovative method that addresses the complex task of estimating the 3D poses of multiple persons in crowded scenes observed from a limited number of calibrated cameras. This challenge is primarily due to the need for accurate cross-view correspondences among often noisy and incomplete 2D pose predictions. Previous methodologies have typically dealt with the problem by reasoning directly in 3D using a pictorial structure model, a process that can be inefficient given the expansive state space involved. The authors propose a more efficient approach that utilizes a convex optimization-based multi-way matching algorithm to cluster detected 2D poses, thereby significantly enhancing both robustness and computation speed without requiring prior knowledge of the number of individuals present.

Technical Contributions and Methodology

The paper introduces several key technical contributions:

1. Multi-Way Matching Algorithm: This algorithm addresses the challenge of establishing consistent correspondences across multiple views, using a combination of geometric consistencies and appearance cues. The innovation lies in solving the matching problem collectively for all views with a cycle-consistency constraint, ensuring globally consistent correspondences. This is formulated as a convex optimization problem, allowing it to efficiently manage missing and false detections.
2. Efficient Pose Estimation Pipeline: By clustering 2D poses into multi-view correspondences, the proposed approach significantly reduces the complexity of the state space, thus improving the efficiency and robustness of subsequent 3D pose inference. The method leverages 2D bounding box and pose detections, followed by 2D pose matching and 3D reconstruction, potentially utilizing a joint-based 3D pictorial structure model for additional structural constraints.
3. Integration of Geometric and Appearance Cues: The effectiveness of using both visual matching and geometric alignment drastically improves performance over strategies relying solely on geometrical methods.

Results and Comparisons

This approach was evaluated using the Campus and Shelf datasets, where it demonstrated superior performance with PCP rates of 96.3% and 96.9%, respectively, compared to state-of-the-art methods that yielded significantly lower accuracy. These results underline the robustness and general applicability of the proposed methodology, especially in handling occlusions and partial view overlaps, common in dynamic multi-person scenarios. The data shows that integrating appearance information enhances the ability to resolve ambiguities present in purely geometry-based approaches. Additionally, the algorithm maintains real-time applicability, crucial for practical deployment where computing resources are constrained.

Implications and Future Directions

The research presents both practical and theoretical advancements, paving the way for more efficient real-time applications in domains such as surveillance, sports analysis, and human-computer interaction. The novel matching strategy offers a promising solution to reduce the computational burdens associated with 3D pictorial structure models. Theoretically, the exploration of joint geometric and appearance cue integration sets a precedent for future research into robust multi-view image analysis.

The paper encourages future exploration in overcoming the presented limitations, such as better handling of severe occlusions or dynamic environments where individuals frequently enter and exit the frame. Additionally, further integration of advanced machine learning methodologies, potentially leveraging unsupervised or semi-supervised learning frameworks, could offer enhancement without requiring extensive labeled datasets. Moreover, the expansion of this method to more generalized non-calibrated camera systems could broaden its applicability to a wider range of use cases.

This paper forms a foundation for subsequent research that could refine these techniques further, driving forward the capabilities of multi-person 3D pose estimation technologies.