Summary
In this chapter, we began by introducing a series of utility scripts, which are reusable Python modules on Kaggle designed for video data manipulation. One such script, video_utils, is used to visualize images from videos and play them. Another script, face_object_detection, utilizes Haar cascade models for face detection.
The third script, face_detection_mtcnn, employs MTCNN models to identify faces and key points such as the eyes, nose, and mouth. We then examined the metadata and video data from the DFDC competition dataset. In this dataset, we applied the aforementioned face detection methods to images from training and test videos, finding the MTCNN model approach to be more robust and accurate, with fewer false positives.
As we near the conclusion of our exploration of data, we will reflect on our journey through various data formats, including tabular, text, image, sound, and now video. We’ve delved into numerous Kaggle datasets and competition datasets...
