Abstract
Human pose estimation is a problem that continues to be one of the greatest challenges in the field of computer vision. While the stacked structure of an hourglass network has enabled substantial progress in human pose estimation and key-point detection areas, it is largely used as a backbone network. However, it also requires a relatively large number of parameters and high computational capacity due to the characteristics of its stacked structure. Accordingly, the present work proposes a more lightweight version of the hourglass network, which also improves the human pose estimation performance. The new hourglass network architecture utilizes several additional skip connections, which improve performance with minimal modifications while still maintaining the number of parameters in the network. Additionally, the size of the convolutional receptive field has a decisive effect in learning to detect features of the full human body. Therefore, we propose a multidilated light residual block, which expands the convolutional receptive field while also reducing the computational load. The proposed residual block is also invariant in scale when using multiple dilations. The well-known MPII and LSP human pose datasets were used to evaluate the performance using the proposed method. A variety of experiments were conducted that confirm that our method is more efficient compared to current state-of-the-art hourglass weight-reduction methods.
Highlights
Human pose estimation is a fundamental method for detecting human behavior, and it is applied in virtual cinematography using computer graphics, human behavior recognition, and building security systems
The well-known human pose estimation datasets MPII and Leeds Sports Poses (LSP) were used to evaluate the performance of the proposed additional interstack skip connection and multidilated light residual blocks
16 coordinates for each joint were labeled for each person
Summary
Human pose estimation is a fundamental method for detecting human behavior, and it is applied in virtual cinematography using computer graphics, human behavior recognition, and building security systems. The traditional method estimates or tracks the human pose using additional equipment, such as depth sensors. The stacked hourglass network [2] is one of the best-known methods for resolving performance problems in human pose estimation. It has a stacked structure of hourglass modules composed of residual blocks [4]. Since the hourglass network performs promisingly in resolving the human pose estimation problem, a number of studies have used it as a backbone or modified the original hourglass network to improve performance [5,6,7,8,9,10]. Ning et al [11] developed a stacked hourglass
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