Recurrent Human Pose Estimation (1605.02914v3)

Abstract: We propose a novel ConvNet model for predicting 2D human body poses in an image. The model regresses a heatmap representation for each body keypoint, and is able to learn and represent both the part appearances and the context of the part configuration. We make the following three contributions: (i) an architecture combining a feed forward module with a recurrent module, where the recurrent module can be run iteratively to improve the performance, (ii) the model can be trained end-to-end and from scratch, with auxiliary losses incorporated to improve performance, (iii) we investigate whether keypoint visibility can also be predicted. The model is evaluated on two benchmark datasets. The result is a simple architecture that achieves performance on par with the state of the art, but without the complexity of a graphical model stage (or layers).

• The paper introduces a recurrent module within a ConvNet that iteratively refines keypoint detection, improving performance in complex poses.
• The paper employs end-to-end training with auxiliary losses to enhance robustness and better handle occluded keypoints.
• The paper demonstrates competitive accuracy on MPII and LSP datasets while reducing complexity compared to models using explicit graphical methods.

Recurrent Human Pose Estimation

The task of estimating human pose in 2D images presents a formidable challenge due to the complexity of body configurations and occlusions. The paper "Recurrent Human Pose Estimation" by Vasileios Belagiannis and Andrew Zisserman contributes a novel approach to this problem, building on the strengths of Convolutional Neural Networks (ConvNets) and incorporating advances inspired by previous models. This work focuses on the effective identification and localization of key body points in images, employing a recurrent module within a ConvNet architecture to enhance performance through iterative refinement.

Contributions and Approach

The proposed model introduces three main innovations:

1. Combined Architecture with Recurrent Module: The model integrates a feed-forward module with a recurrent module, the latter running iteratively to improve performance. This design significantly increases the effective receptive field of the network, allowing it to capture more contextual information pertinent to pose estimation.
2. Training Methodology: The model supports end-to-end training from scratch, augmented by auxiliary losses which enhance optimization and robustness during training.
3. Investigation of Occlusion Handling: The paper offers an initial exploration into predicting keypoint visibility as a complementary goal to pose estimation, providing insights into handling occlusion issues commonly faced in real-world scenarios.

Model Evaluation

The presented model was tested on the MPII Human Pose and LSP datasets, achieving results that are competitive with the state-of-the-art methods at the time. Importantly, the proposed approach does not utilize an explicit graphical model, reducing complexity while maintaining efficacy. Various training regimes concerning occluded keypoints were evaluated, revealing that including occluded keypoints as a training objective enhances performance by increasing the training set size.

Implications and Future Directions

This research highlights the potential of recurrent neural architectures in understanding complex visual configurations without relying on intricate graphical models. The model's success points to the possibility of constructing simpler, more interpretable networks capable of sophisticated tasks such as human pose estimation. The reduced parameter count in comparison to other models signifies computational efficiency benefits and potential applicability in resource-constrained environments.

Possible future directions include improving occlusion prediction by further refining the use of combined keypoint and body-part heatmaps. Investigating their ability to prevent errors, such as incorrect limb assignments, could enhance the model's accuracy and reliability. This research opens avenues for more extensive exploration of recurrent architectures in vision tasks, suggesting stability and performance gains across varying configurations and domains in computer vision.

Overall, the paper makes a solid contribution to the field of human pose estimation, showcasing the power of recurrent networks in capturing contextual dependencies and advancing the state-of-the-art in keypoint detection and localization.