A Novel One-Stage End-to-End Approach for Efficient and Accurate Multi-Person Pose Estimation

Khái niệm cốt lõi

A novel one-stage end-to-end algorithm called Joint Coordinate Regression and Association (JCRA) that directly predicts full-body poses without any post-processing, achieving state-of-the-art performance in accuracy and efficiency.

Tóm tắt

The paper introduces a novel one-stage end-to-end multi-person 2D pose estimation algorithm called Joint Coordinate Regression and Association (JCRA). The key highlights are:

1. JCRA directly predicts full-body pose coordinates without requiring any post-processing steps like keypoint grouping, non-maximum suppression, or heatmap refinement. This simplifies the pipeline and improves efficiency.

2. JCRA employs a symmetric network architecture with an equal number of encoder and decoder layers, which ensures high accuracy in identifying keypoints by effectively translating abstractions back into concrete forms.

3. Extensive experiments on the MS COCO and CrowdPose benchmarks demonstrate that JCRA outperforms state-of-the-art approaches in both accuracy and efficiency. JCRA achieves 69.2 mAP on COCO, surpassing previous one-stage end-to-end methods, and is 78% faster at inference than previous state-of-the-art bottom-up algorithms.

4. JCRA is robust and can handle a wide range of poses, including viewpoint changes, occlusions, and crowded settings, making it suitable for real-world applications.

Thống kê

JCRA achieves 69.2 mAP on the COCO val2017 dataset.
JCRA is 78% faster at inference than previous state-of-the-art bottom-up algorithms.

Trích dẫn

None

Thông tin chi tiết chính được chắt lọc từ

Joint Coordinate Regression and Association For Multi-Person Pose Estimation, A Pure Neural Network Approach

by Dongyang Yu,... lúc arxiv.org 04-22-2024

https://arxiv.org/pdf/2307.01004.pdf

Yêu cầu sâu hơn

How can JCRA's performance be further improved, especially for medium-sized targets, to match the accuracy of top-down methods?

To enhance JCRA's performance, particularly for medium-sized targets, several strategies can be implemented:

Refinement Mechanisms: Introducing refinement mechanisms in the keypoint decoder can help improve the accuracy of keypoint predictions, especially for medium-sized targets. This can involve incorporating additional layers or modules that focus on refining the localization of keypoints based on contextual information.

Data Augmentation: Augmenting the training data with more examples of medium-sized targets can help the model learn to better localize keypoints in such scenarios. This can include augmenting the dataset with various scales, poses, and occlusions to make the model more robust.

Loss Function Optimization: Fine-tuning the loss function to give more weight to medium-sized targets during training can guide the model to focus on improving accuracy specifically for these instances. Adjusting the loss function to address the challenges specific to medium-sized targets can lead to better performance.

Architectural Adjustments: Making architectural adjustments, such as increasing the depth or width of certain layers in the network that are crucial for handling medium-sized targets, can help in capturing more intricate details and nuances in the pose estimation process.

Ensemble Methods: Implementing ensemble methods by combining multiple JCRA models trained with different initializations or hyperparameters can help in capturing diverse patterns and improving overall performance, especially for medium-sized targets.

How can JCRA's architecture and training be adapted to enable joint detection of human poses and bounding boxes, similar to Mask R-CNN, to further enhance its applicability in real-world scenarios?

To adapt JCRA's architecture and training for joint detection of human poses and bounding boxes, similar to Mask R-CNN, the following steps can be taken:

Integration of Object Detection Head: Modify the JCRA architecture to include an object detection head that can predict bounding boxes around individuals in the image. This additional head can be connected to the existing architecture to enable the joint detection of poses and bounding boxes.

Multi-Task Learning: Implement multi-task learning during training, where the model is trained to simultaneously optimize for both pose estimation and object detection tasks. This can be achieved by combining the loss functions of both tasks and updating the model parameters accordingly.

Bounding Box Regression: Introduce a bounding box regression module in the network that can predict the coordinates of bounding boxes around detected individuals. This module can be trained alongside the pose estimation components to learn the spatial relationships between poses and bounding boxes.

Fine-Tuning Training Data: Fine-tune the training data to include annotations for both human poses and bounding boxes. This annotated dataset can be used to train the model in a supervised manner to learn the joint detection task effectively.

Evaluation Metrics: Define appropriate evaluation metrics that consider the accuracy of both pose estimation and bounding box detection. This will ensure that the model's performance is assessed comprehensively for real-world applications.

By incorporating these adaptations, JCRA can be transformed into a comprehensive framework capable of joint detection of human poses and bounding boxes, enhancing its utility and effectiveness in various real-world scenarios.