# Ball Tracking Based on Multiscale Feature Enhancement and Cooperative Trajectory Matching
###### Abstract  
Most existing object tracking research focuses on pedestrians and autonomous driving while ignoring sports scenes. When general object tracking models are used for ball tracking, there are often problems, such as detection missions due to small object sizes and trajectory loss due to occlusion. To address these challenges, we propose a ball detection and tracking model called HMMATrack based on multiscale feature enhancement and multilevel collaborative matching to improve ball-tracking results from the entire process of sampling, feature extraction, detection, and tracking. It includes a Heuristic Compound Sampling Strategy to deal with tiny sizes and imbalanced data samples; an MNet-based detection module to improve the ball detection accuracy; and a multilevel cooperative matching and automatic trajectory correction tracking algorithm that can quickly and accurately correct the ball's trajectory. We also hand-annotated SportsTrack, a ball-tracking dataset containing soccer, basketball, and volleyball scenes. Extensive experiments are conducted on the SportsTrack, demonstrating that our proposed HMMATrack model outperforms other representative state-of-the-art models in ball detection and tracking. ball tracking; small object detection; multiscale feature; multilevel collaborative matching +
Footnote †: journal: Medical Image Analysis  
0000-0001-6000-0001-6000][PERSON]  
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of the ball detector. [PERSON] et al. [4; 5] added a classification confidence branch to the detection model, obtained the detection bounding box, and divided the image blocks into three categories, balls, players, and backgrounds, making the positioning of balls more accurate in the tracking phase. [PERSON] [6] used a method based on a fully convolutional neural network with automatic encoding and decoding structure: first, generate a ball candidate area on the original image [7], then calculate the distance between the candidate area and the truth ball, delete the wrong candidate area by setting a threshold, and detect the model output containing the confidence of the ball and the diameter of the ball. In the tracking stage, existing research mainly focuses on solving the problems of motion blur and deformation caused by the fast movement of balls and trajectory incoherence caused by occlusion and out-of-drawing. Using a bounding box overlap probability measurement algorithm [4] can generate a more robust ball trajectory from tracking loss and recovery; estimating the ball's position through the extended Kalman filter [8] can better cope with sports videos. Uncertainty in ball movement: the ball's motion state is modeled based on time-varying fission filters [9], and the relative space filter is used to correct the trajectory, integrating spatiotemporal information. [PERSON] et al. [10] consider the volleyball flight process. The most influential air resistance and gravity factors are used to simulate the ball's motion state, and a trajectory correction algorithm based on time-motion characteristics is proposed. The WITHDRAW model [11] combines center-of-mass tracking with an improved Kalman filter to estimate the ball's trajectory accurately. [PERSON] et al. [12] combined the MeanShift algorithm with adaptive object area size to improve Kalman filter tracking estimation, improving tracking accuracy and speed. [PERSON] et al. [13] utilize Harris corner detection, SURF feature extraction, and a particle swarm optimization algorithm to improve basketball tracking results. [PERSON] et al. [14] preset a fixed basketball size and search for basketball position points in the scene using RANSAC (Random Sample Consensus). In a posterior step, the sphere center is fitted using z-score values eliminating outliers from the sphere. [PERSON] [15] embed graph convolution to effectively aggregate deep features. On the other hand, most recent sports video detection and tracking techniques directly adapt general detection and tracking methods to sports-specific applications.