Human pose estimation in crowded scenes has always been a challenging task in bottom-up multi-person pose estimation. To improve the accuracy of pose estimation in dense crowds, we propose an improved bottom-up human pose estimation model called H-DEKR, which is based on Disentangled Keypoint Regression for Bottom-Up Human Pose Estimation (DEKR). The model first enhances the coarse/fine-grained feature extraction abilities of the backbone (HRNet) by introducing different structures of Polarized Self-attention (PSA). Then, Pyramid Convolution (PyConv) is introduced to extract multi-scale information, alleviating the problem of uneven human scales. Results show that our model based on HRNet-W32 achieves accuracy of 67.1% on the CrowdPose dataset, which is 1.4% higher than the DEKR, respectively. Therefore, the proposed model in this paper is able to improve the accuracy of human pose estimation in dense crowds.
This paper describes a technique that inspects the dust emission volume by scattering-light energy characteristic of the dust particles. The scattering-light energy characteristic information, including total energy, peek value energy and the positions of peek value energy, are extracted from the scattering light received by a two-dimension cuneal image sensor. A common inspecting principle of dust emission volume and the two-dimension cuneal image sensor that realizes the measurement of scattering-light energy are discussed. According to the characteristic that the real part of refractive index influences less on the normalized distribution of scattering light the angle smaller than 30°, the center of a two-dimension cuneal image sensor is placed at the optical axis of focal plane of Fourier lens and is capable of receiving scattering light energy within forward angle 2.8°. Experiments were made with a simulating dust emitting experimental instrument and obtained scattering light energy characteristic information for dust emission volume 50mg, 100mg, 150mg, 200mg, 300mg, 400mg, 500mg and 600mg. We point out that dust emission volume can be inspected by scattering-light energy characteristic, and suggest that it is necessary to establish a database that shows the relationship between dust emission volume and scattering-light energy and to measure the size distribution of dust particles.
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