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Poz tahmini, kamera ile çekilen görüntülerde insan iskeletindeki anahtar noktaların piksel konumlarının tespit edilmesi amacıyla ortaya çıkmıştır. Poz tahmini yöntemlerinin çıktıları görüntüde tespit edilen tüm eklem noktalarının piksel değerlerini ilişkilendirdiği kişiye göre vermektedir. Videolarda kişilerin hareketlerini anlamlandırmak için ardışık görüntü kareleri boyunca kimliklendirilmeleri gerekir. Böylece kişilerin video boyunca ne zaman hangi hareketleri yaptığı tespit edilebilir. Bu çalışmada sabit hızlı ve sabit ivmeli hareket modeline göre Kalman filtresi kullanarak küresel en yakın komşu (KEYK) algoritması ile tasarlanan çok kişili poz takibi yönteminin verdiği sonuçlar incelenmiştir. Geliştirilen ön işleme adımlarının poz tahmini yöntemlerinin kalitesini artırarak poz takibine etkisi de tespit edilmiştir. Bu amaçla PoseTrack veri kümesi üzerinde DCPose ve OpenPose poz tahmini yöntemlerinin başarımı değerlendirilmiştir. Ön işleme adımları ile sistemin başarımının her iki yöntem için de yükseldiği görülmüştür. Gerçek zamanlı çalışabilen, başarılı bir poz tahmini yöntemi olan ve düşük kaynak tüketimine sahip OpenPose yöntemi ile literatürde en iyi sonuçları veren DCPose yönteminin sonuçları incelendiğinde çok kişili poz takibi konusunda DCPose yönteminin daha başarılı sonuçlar verdiği görülmüştür. 550 farklı video ile elde edilen sonuçlar ön işleme adımları uygulandığında başarımı sabit hızlı ve sabit ivmeli hareket modellerinde aşağıdan yukarı yöntemi OpenPose için %22.6 ve %16.02, yukarıdan aşağı yöntemi DCPose için %21.2 ve %21.8 artırmıştır.
The position forecast was revealed in the images taken by the camera to identify the piksel positions of the key points in the human skeleton. Pos estimation methods give outcomes according to the person in which all the joint points identified in the image relate the pixel values. They need to be identified along the sequential image squares to understand the movements of people in the videos. This allows people to identify what movements they are doing during the video. In this study, the results of the multi-personal position tracking method designed with the global closest neighbor (KEYK) algorithm using the Kalman filter according to the fixed fast and fixed drive movement model were studied. The advanced processing steps have also been identified by improving the quality of the poses forecast methods. For this purpose, the success of DCPose and OpenPose positions forecast methods on the PoseTrack data set has been assessed. The advanced processing steps and the success of the system have been shown to increase for both methods. The results of the DCPose method, which can work in real time, a successful position estimation method and a low resource consumption, and the results of the DCPose method, which gives the best results in literature, have been studied, and the DCPose method has been shown to give more successful results in multi-personal position tracking. The results obtained with 550 different videos are implemented in pre-processing steps when success is made in fixed fast and fixed-driving movement models from the bottom to the top method for OpenPose 22%. 6 and 16.02 percent, the method from above to below increased 21.2 percent and 21.8 percent for DCPose.
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.
Alan : Mühendislik
Dergi Türü : Uluslararası
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