Human steering angle estimation in video based on key point detection and Kalman filter

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.