Kalman Filtresi ve Küresel En Yakın Komşu Yöntemi ile Çok Kişili Gerçek Zamanlı Poz Takibi

Abstract

Pose estimation has emerged in order to detect pixel positions of keypoints on the human skeleton in images taken with the camera. The outputs of the pose estimation methods give the pixel values of all the articulation points detected in the image according to the person they associate with. In order to make sense of people's movements in videos, people need to be identified across successive frames. Thus, it can be determined when people make which movements during the video. In this study, the results of a multi-person exposure tracking method that is designed with the global nearest neighbor (GNN) algorithm using the Kalman filter based on constant velocity and constant acceleration motion models were examined. The effect of the developed preprocessing steps that increase the quality of the pose estimation methods on the pose tracking has also been determined. For this purpose, the performance of DCPose and OpenPose pose estimation methods on the PoseTrack dataset was evaluated. It was observed that the performance of the system increased for both methods with the preprocessing steps. When the results of OpenPose method, which can work in real time, a successful pose estimation method and have low resource consumption, and DCPose method, which gives the best results in the literature, are examined, it is seen that DCPose method gives better results in multi-person pose tracking. The results obtained with 550 different video increased the performance in constant velocity and constant acceleration motion models by 22.6% and 16.02% for bottom-up method OpenPose and 21.2% and 21.8% for top-down method DCPose when preprocessing steps were applied.