Update README.md

README.md

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-license: gpl-3.0
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+---
+license: gpl-3.0
+task_categories:
+- tabular-classification
+- tabular-regression
+- image-to-3d
+- depth-estimation
+pretty_name: AD Trajectories
+size_categories:
+- 100K<n<1M
+---
+
+**Paper in the making**
+
+---
+
+# AD-Trajectories Dataset
+This dataset was created for the Master's thesis "From Broadcast to 3D: A Deep Learning Approach for Tennis Trajectory and Spin Estimation" by Alexandra Göppert at the University Augsburg, Chair of Machine Learning and Computer Vision.
+The AD-Trajectories dataset is a large-scale synthetic dataset generated using the MuJoCo physics engine. It was built to bridge the synthetic-to-real gap by providing highly accurate physical models of aerodynamic forces, such as the Magnus effect, and complex ball-court interactions.
+
+---
+## Dataset Overview
+The dataset comprises exactly 187,200 individual synthetic tennis trajectories. All physical kinematics, including the 3D positions, linear velocities, and angular velocities (spin), are captured at a high resolution of 500 frames per second (fps), corresponding to a time step of 0.002 seconds.
+
+---
+# Folder Structure & Metadata
+Unlike datasets that use a separate JSON or CSV file for labels, the metadata for each trajectory is encoded directly into its folder path. The repository utilizes a four-tier nested folder structure categorized by the specific properties of the shot:
+
+    Base Stroke (Hit Type): The top-level folder defines the stroke. There are 7 options: groundstroke, lob, serve, short, smash, toss, or volley.
+    Direction of Flight: The second tier defines the trajectory's direction, organized into close_to_far and far_to_close subfolders. Close_to_far means that the ball is flying in the negative x-axis direction. Far_to_close means the ball flies in the positce x-axis direction. 
+    Status (In/Out): The third tier indicates whether the ball's first bounce landed validly inside the court (in) or outside the lines (out).
+    Trajectory ID: The final folder contains the isolated data for a single shot, numbered sequentially (e.g., trajectory_0000, trajectory_0001, ...).
+
+Example Path: groundstroke/close_to_far/in/trajectory_0000/
+
+---
+# Data Structures per Trajectory
+Inside each trajectory_xxxx folder, you will find exactly seven .npy files. These numpy arrays store the spatial, temporal, and camera data for that specific sequence:
+
+    positions.npy: The 3D position of the ball (x, y, z) throughout the trajectory, recorded at a resolution of 0.002s.
+    velocities.npy: The linear velocity of the ball relative to the world coordinate system, recorded at a resolution of 0.002s.
+    rotations.npy: The angular velocity (spin) of the ball in all 3 directions, recorded at a resolution of 0.002s.
+    times.npy: A 1D array containing the corresponding timestamps for the positions, velocities, and rotations arrays.
+    bounces.npy: Includes the exact timestamps of the bounces occurring during the trajectory, where t=0 represents the start of the trajectory.
+    Mint.npy: The intrinsic camera matrix.
+    Mext.npy: The extrinsic camera matrix.
+
+The position and velocity is defined in relation to the 3D world coordinate system, which is defined like follows:
+<img src="./world_origin.png" alt="Coordinate system definition of 3D world coordinates" style="width:50%; height:auto;" >
+
+The ball spin (rotations.npy) is defined in relation to the ball's local coordinate system. The direction of  which is defined as follows:
+<img src="./ball_origin.png" alt="Definition of the ball's local coordinate system" style="width:50%; height:auto;" >
+
+Note on 2D Image Projections: To save space and reduce redundancy, this dataset does not include pre-calculated 2D image projections (u,v). Because both the intrinsic (Mint) and extrinsic (Mext) camera matrices are provided for every trajectory, users can easily calculate the 2D image projections themselves by multiplying the 3D world coordinates by the provided camera matrices.