FasterPose: A Faster Simple Baseline for Human Pose Estimation

Author:

Dai Hanbin1,Shi Hailin1,Liu Wu1,Wang Linfang1,Liu Yinglu1,Mei Tao1

Affiliation:

1. JD AI Research, Beijing, China

Abstract

The performance of human pose estimation depends on the spatial accuracy of keypoint localization. Most existing methods pursue the spatial accuracy through learning the high-resolution (HR) representation from input images. By the experimental analysis, we find that the HR representation leads to a sharp increase of computational cost, while the accuracy improvement remains marginal compared with the low-resolution (LR) representation. In this article, we propose a design paradigm for cost-effective network with LR representation for efficient pose estimation, named FasterPose. Whereas the LR design largely shrinks the model complexity, how to effectively train the network with respect to the spatial accuracy is a concomitant challenge. We study the training behavior of FasterPose and formulate a novel regressive cross-entropy (RCE) loss function for accelerating the convergence and promoting the accuracy. The RCE loss generalizes the ordinary cross-entropy loss from the binary supervision to a continuous range, thus the training of pose estimation network is able to benefit from the sigmoid function. By doing so, the output heatmap can be inferred from the LR features without loss of spatial accuracy, while the computational cost and model size has been significantly reduced. Compared with the previously dominant network of pose estimation, our method reduces 58% of the FLOPs and simultaneously gains 1.3% improvement of accuracy. Extensive experiments show that FasterPose yields promising results on the common benchmarks, i.e., COCO and MPII, consistently validating the effectiveness and efficiency for practical utilization, especially the low-latency and low-energy-budget applications in the non-GPU scenarios.

Funder

National Key R&D Program of China

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications,Hardware and Architecture

Reference37 articles.

1. 2D Human Pose Estimation: New Benchmark and State of the Art Analysis

2. Adrian Bulat and Georgios Tzimiropoulos. 2016. Human pose estimation via convolutional part heatmap regression.

3. Yuanhao Cai Zhicheng Wang Zhengxiong Luo Binyi Yin Angang Du Haoqian Wang Xinyu Zhou Erjin Zhou Xiangyu Zhang and Jian Sun. 2020. Learning delicate local representations for multi-person pose estimation. Retrieved from https://arXiv:2003.04030.

4. Realtime Multi-person 2D Pose Estimation Using Part Affinity Fields

5. Cascaded Pyramid Network for Multi-person Pose Estimation

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