Ball Tracking, Event Detection, Video Summarization of
Football
Vishwa Mangal Mentor: Dr. Aditya Nigam
• Ball Tracking done using STRUCT (structured output for kernel
Tracking).
• Ground truth for the first few frames are given and then it
tracks the ball in subsequent frames.
• Stores the features from the ground truth and searches in
radius of “R” which best match the feature.
• Audio summarization using PRAAT language.
• High Pitch implies the important moment of match.
• The threshold was found at 60% of the mean pitch.
Raw Data collection of Forehand,
Backhand, and Service
Prediction of player using
YOLO in each frame.
optical flow between the
frames
Using MBH to remove the
noise.
Train using 3D - CNN
3D - CNN
Working Of YOLO
YOU ONLY LOOK ONCE
Data Specification
• Australian open final 2017.
• video to frame at 15FPS.
• Manually classified frames into forehand, backhand,
service.
• 30 instances each of forehand, backhand, and service.
Performance
• Train/Test split at 50/10 for 2 class and 80/10 for three
class.
• On testing with two class i.e forehand and backhand, we got
the 100% accuracy.
• On testing with three class we got the accuracy of 70%.
Future Work
• Classify the s t r o k e s i n u n t r i m m e d videos using
T - CNN.
100% accuracy with 2 class.
70% accuracy with 3 class .
