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| Human steering angle estimation in video based on key point detection and Kalman filter |
| YanpengHu1,YuxuanLiu1,YanguangXu2,YinghuiWang1 |
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| (1 School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China;2 KE Holdings Inc., Beijing 100085, China) |
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| 摘要: |
| Human pose recognition and estimation in video is pervasive. However, the process noise and local occlusion bring great
challenge to pose recognition. In this paper, we introduce the Kalman filter into pose recognition to reduce noise and solve
local occlusion problem. The core of pose recognition in video is the fast detection of key points and the calculation of human
steering angles. Thus, we first build a human key point detection model. Frame skipping is performed based on the Hamming
distance of the hash value of every two adjacent frames in video. Noise reduction is performed on key point coordinates with
the Kalman filter. To calculate the human steering angle, current state information of key points is predicted using the optimal
estimation of key points at the previous time. Then human steering angle can be calculated based on current and previous state
information. The improved SENet, NLNet and GCNet modules are integrated into key point detection model for improving
accuracy. Tests are also given to illustrate the effectiveness of the proposed algorithm. |
| 关键词: Key point detection · Part affinity fields · Kalman filter · Human steering angle |
| DOI：https://doi.org/10.1007/s11768-022-00100-3 |
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| 基金项目:This work was supported by the National Natural Science Foundation of China (Nos. 72101026, 61621063) and the State Key Laboratory of Intelligent Control and Decision of Complex Systems. |
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| Human steering angle estimation in video based on key point detection and Kalman filter |
| Yanpeng Hu1,Yuxuan Liu1,Yanguang Xu2,Yinghui Wang1 |
| (1 School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China;2 KE Holdings Inc., Beijing 100085, China) |
| Abstract: |
| Human pose recognition and estimation in video is pervasive. However, the process noise and local occlusion bring great
challenge to pose recognition. In this paper, we introduce the Kalman filter into pose recognition to reduce noise and solve
local occlusion problem. The core of pose recognition in video is the fast detection of key points and the calculation of human
steering angles. Thus, we first build a human key point detection model. Frame skipping is performed based on the Hamming
distance of the hash value of every two adjacent frames in video. Noise reduction is performed on key point coordinates with
the Kalman filter. To calculate the human steering angle, current state information of key points is predicted using the optimal
estimation of key points at the previous time. Then human steering angle can be calculated based on current and previous state
information. The improved SENet, NLNet and GCNet modules are integrated into key point detection model for improving
accuracy. Tests are also given to illustrate the effectiveness of the proposed algorithm. |
| Key words: Key point detection · Part affinity fields · Kalman filter · Human steering angle |
