Feature extraction algorithm for fast moving pedestrians with frame drop constraint based on deep learning. (21st October 2019) - Ma, Mei; Hu, Yaomin
When the existing method extracts the information of the fast moving pedestrian, the frame dropping phenomenon may occur, resulting in low extraction precision. A fast moving pedestrian frame loss constrained feature extraction algorithm based on depth tilt is proposed. Block matching and denoising are performed on the pedestrian image. The contour feature extraction method is used to reconstruct the adjacent frames and the reconstructed image frame vector is sub-block fusion. The depth learning algorithm is used to extract the feature quantity of the gray pixel from the frame falling part of the image. Improved feature extraction algorithm for pedestrians with frame loss constraints. The simulation results show that the standard deviation of the frame loss of the extraction result is 8.235 and the standard deviation of the non-drop frame is 4.353. It proves that the algorithm has low frame loss rate and high extraction and recognition ability.