Rethinking Keypoint Representations: Modeling Keypoints and Poses as Objects for Multi-Person Human Pose Estimation (2111.08557v4)

Abstract: In keypoint estimation tasks such as human pose estimation, heatmap-based regression is the dominant approach despite possessing notable drawbacks: heatmaps intrinsically suffer from quantization error and require excessive computation to generate and post-process. Motivated to find a more efficient solution, we propose to model individual keypoints and sets of spatially related keypoints (i.e., poses) as objects within a dense single-stage anchor-based detection framework. Hence, we call our method KAPAO (pronounced "Ka-Pow"), for Keypoints And Poses As Objects. KAPAO is applied to the problem of single-stage multi-person human pose estimation by simultaneously detecting human pose and keypoint objects and fusing the detections to exploit the strengths of both object representations. In experiments, we observe that KAPAO is faster and more accurate than previous methods, which suffer greatly from heatmap post-processing. The accuracy-speed trade-off is especially favourable in the practical setting when not using test-time augmentation. Source code: https://github.com/wmcnally/kapao.

• The paper presents KAPAO, an innovative method that models keypoints and poses as objects, reducing reliance on heatmap-based regression.
• It employs a YOLO-style feature extractor with a shared network head to predict keypoints and poses simultaneously, streamlining the detection process.
• Experiments on COCO and CrowdPose datasets demonstrate improved inference speed and robustness in crowded scenes compared to conventional methods.

Modeling Keypoints and Poses as Objects for Multi-Person Human Pose Estimation

This paper proposes a novel approach to human pose estimation by reimagining keypoint detection through the lens of object detection. The method, named KAPAO (Keypoints And Poses As Objects), leverages a dense single-stage anchor-based detection framework to treat both individual keypoints and entire poses as distinct objects. This paradigm shift addresses some inherent inefficiencies of the conventional heatmap-based keypoint regression, which suffers from quantization errors and requires computationally expensive post-processing.

Methodology

KAPAO redefines keypoint estimation by detecting keypoints and poses simultaneously. The method introduces a new representation for poses, extending conventional object detection techniques to encompass keypoints. This dual approach allows the system to use a shared network head to predict both keypoints as objects and poses as compound objects, ensuring a more unified and streamlined process.

Architectural Details

KAPAO employs a YOLO-style feature extractor within a feature pyramid macroarchitecture. Models of various sizes (KAPAO-S/M/L) are designed by altering the number of layers and channels, providing flexibility in balance between speed and accuracy. The network's output covers the spatial attributes of predicted poses and keypoints, optimized through specific loss functions for objects, bounding boxes, classes, and pose keypoints.

Inference and Efficiency

An essential aspect of KAPAO is its efficient inference process, which involves transforming outputs back to image space and using non-maximum suppression to filter detections. The computational efficiency stems primarily from a novel matching algorithm that fuses keypoint and pose detections, effectively exploiting both detection types' strengths without significant speed trade-offs.

Experimental Results

The evaluation on COCO and CrowdPose datasets demonstrates KAPAO's superior performance in terms of speed and accuracy over established single-stage methods. Without test-time augmentation, KAPAO achieves competitive accuracy while eliminating the substantial computational overhead typical of heatmap-based techniques.

Key Findings

• Accuracy-speed trade-off: KAPAO considerably reduces inference time with a negligible decrease in accuracy, a notable improvement over previous methods that rely heavily on post-processing.
• Robustness in Crowded Scenes: On the CrowdPose dataset, KAPAO excels by providing accurate results even in occluded conditions, highlighting its robustness in real-world scenarios.
• Error Analysis: The architecture reduces common errors such as swap and inversion by modeling cohesive poses, showcasing an improvement in instance detection accuracy.

Implications and Future Directions

This research lays the groundwork for more efficient pose estimation systems by simplifying the detection process through object representations. The approach has implications beyond human pose estimation and could extend to domains such as facial landmark detection and other applications requiring precise spatial localization of features.

Future work could explore further enhancements in keypoint localization precision and extend the methodology to incorporate more complex pose attributes. Additionally, leveraging advancements in neural architectures could further optimize KAPAO, enabling even broader applicability in AI-driven vision systems. The potential for this framework is significant, pointing towards an evolution in the efficiency and capability of pose estimation methodologies.