Multi-scale Aggregation R-CNN for 2D Multi-person Pose Estimation
This addresses computational bottlenecks for researchers and practitioners in computer vision working on real-time or resource-constrained multi-person pose estimation applications.
The paper tackles the computational inefficiency of top-down multi-person pose estimation methods by proposing MSA R-CNN, which integrates human detection and keypoint localization in a single model while effectively using multi-scale information. The model achieved the best performance among single model-based methods with comparable results to separated model-based methods using less computation.
Multi-person pose estimation from a 2D image is challenging because it requires not only keypoint localization but also human detection. In state-of-the-art top-down methods, multi-scale information is a crucial factor for the accurate pose estimation because it contains both of local information around the keypoints and global information of the entire person. Although multi-scale information allows these methods to achieve the state-of-the-art performance, the top-down methods still require a huge amount of computation because they need to use an additional human detector to feed the cropped human image to their pose estimation model. To effectively utilize multi-scale information with the smaller computation, we propose a multi-scale aggregation R-CNN (MSA R-CNN). It consists of multi-scale RoIAlign block (MS-RoIAlign) and multi-scale keypoint head network (MS-KpsNet) which are designed to effectively utilize multi-scale information. Also, in contrast to previous top-down methods, the MSA R-CNN performs human detection and keypoint localization in a single model, which results in reduced computation. The proposed model achieved the best performance among single model-based methods and its results are comparable to those of separated model-based methods with a smaller amount of computation on the publicly available 2D multi-person keypoint localization dataset.