Lighter Stacked Hourglass Human Pose Estimation
Ahmed Elhagry, Mohamed Saeed, Musie Araia

TL;DR
This paper introduces a lighter version of the stacked hourglass network for human pose estimation, analyzing how architectural changes impact its speed and accuracy in identifying human joints.
Contribution
It proposes modifications to the original architecture to improve computational efficiency while maintaining accuracy in human pose estimation.
Findings
Architectural modifications can enhance speed without sacrificing accuracy.
Multi-scale processing captures important cues like limb orientation and joint relationships.
A lighter model maintains competitive performance in pose estimation tasks.
Abstract
Human pose estimation (HPE) is one of the most challenging tasks in computer vision as humans are deformable by nature and thus their pose has so much variance. HPE aims to correctly identify the main joint locations of a single person or multiple people in a given image or video. Locating joints of a person in images or videos is an important task that can be applied in action recognition and object tracking. As have many computer vision tasks, HPE has advanced massively with the introduction of deep learning to the field. In this paper, we focus on one of the deep learning-based approaches of HPE proposed by Newell et al., which they named the stacked hourglass network. Their approach is widely used in many applications and is regarded as one of the best works in this area. The main focus of their approach is to capture as much information as it can at all possible scales so that a…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsHuman Pose and Action Recognition · Video Surveillance and Tracking Methods · Anomaly Detection Techniques and Applications
