A Novel Changing Athlete Body Real-Time Visual Tracking Algorithm Based on Distractor-Aware SiamRPN and HOG-SVM

Abstract

Athlete detection in sports videos is a challenging task due to the dynamic and cluttered background. Distractor-aware SiamRPN (DaSiamRPN) has a simple network structure and can be utilized to perform long-term tracking of large data sets. However, similarly to the Siamese network, the tracking results heavily rely on the given position in the initial frame. Hence, there is a lack of solutions for some complex tracking scenarios, such as running and changing of bodies of athletes, especially in the stage from squatting to standing to running. The Haar feature-based cascade classifier is involved to catch the key frame, representing the video frame of the most dramatic changes of the athletes. DaSiamRPN is implemented as the tracking method. In each frame after the key frame, a detection window is given based on the bounding box generated by the DaSiamRPN tracker. In the new detection window, a fusion method (HOG-SVM) combining features of Histograms of Oriented Gradients (HOG) and a linear Support-Vector Machine (SVM) is proposed for detecting the athlete, and the tracking results are updated in real-time by fusing the tracking results of DaSiamRPN and HOG-SVM. Our proposed method has reached a stable and accurate tracking effect in testing on men’s 100 m video sequences and has realized real-time operation.

3D zebrafish tracking with topology association

“…Sheng et al. [52] implemented HOG and SVM for detecting athletes and DaSiamRPN for tracking, these method reached a stable and accurate tracking effect in testing on men's 100 m video sequences. Zhang et al.…”

Modeling and Stability Analysis of Coarse–Fine Composite Mechatronic System in UAV Multi-Gimbal Electro-Optical Pod

“…,, Figure 12 shows the kinematics coupling relationship, which is abstracted from the parameters in the Euler rigid body dynamics expressed in Equations ( 17)- (19). The relationships of mechanical structure parameter optimization are divided into three stages: external environment disturbance, coarse motor (USM) stator, and fine motor (VCM) rotor.…”

“…In order to simplify the analysis process of coarse-fine stage visual axis stabilization, this paper mainly discusses the conduction path and characteristics of UAV motion in relation to the coarse-fine composite mechanical structure. Therefore, the disturbance input of the external environment is analyzed as an inertial coordinate system, and the whole process of motion coupling of the coarse-fine mechatronic system is obtained Figure 12 shows the kinematics coupling relationship, which is abstracted from the parameters in the Euler rigid body dynamics expressed in Equations ( 17)- (19). The relationships of mechanical structure parameter optimization are divided into three stages: external environment disturbance, coarse motor (USM) stator, and fine motor (VCM) rotor.…”

“…In order to further study the parameter optimization mechanism of the two-axis four-gimbal structure, Figure 14 was drawn based on the Euler kinematics coupling of Equations ( 24)-( 27) and Euler rigid body dynamics of Equations ( 17)- (19). In Figure 14, the torques caused by the kinematic parameters of the gimbals and the geometric coupling parameters are combined.…”

A Learning Frequency-Aware Feature Siamese Network for Real-Time Visual Tracking

“…Existing deep learning-based trackers can obtain robust tracking results for deep models [1][2][3][4] have strong foreground and background discrimination ability by learning knowledge with massive parameters. However, targets are changing during the tracking process and these models perform heavy calculations to adapt to the current target, which limits the tracking speed and cannot meet the requirements of real-time tracking.…”