Detection of Audio-Video Synchronization Errors Via Event Detection
This addresses audio-video synchronization issues for tennis video analysis, but it is incremental as it applies existing deep learning methods to a specific domain.
The paper tackles the problem of detecting audio-video synchronization errors in tennis videos by training deep networks to detect visual and auditory signatures of ball hits, achieving high accuracy on a large-scale database of 504,300 frames from 6 hours of video with simulated errors.
We present a new method and a large-scale database to detect audio-video synchronization(A/V sync) errors in tennis videos. A deep network is trained to detect the visual signature of the tennis ball being hit by the racquet in the video stream. Another deep network is trained to detect the auditory signature of the same event in the audio stream. During evaluation, the audio stream is searched by the audio network for the audio event of the ball being hit. If the event is found in audio, the neighboring interval in video is searched for the corresponding visual signature. If the event is not found in the video stream but is found in the audio stream, A/V sync error is flagged. We developed a large-scaled database of 504,300 frames from 6 hours of videos of tennis events, simulated A/V sync errors, and found our method achieves high accuracy on the task.