This self-editing video uses machine learning algorithms to cut out the boring parts of security footage and consumer videos. Developed by Carnegie Mellon University in Pennsylvania, LiveLight can automatically edit film reels to create a montage of interesting moments.
The self-editing video technology contains a 'learned dictionary' that can be taught to recognize repetitive moments and limited activity and cut it out. This algorithm allows LiveLight to scan footage and pick out events, which it compares and contrasts to keep the more active or new sequences. Springwise uses the example of a long static shot of cars driving by. It would be reduced to a shorter clip because it's repetitive, versus a car accident which would be recognized as a new event.
Self-Editing Video Montages
LiveLight Uses Machine Learning to Cut the Boring Parts of Film Reels
Trend Themes
1. Self-editing Video Montages - Machine learning algorithms automatically edit film reels to create montages of interesting moments.
2. Machine Learning Algorithms - Machine learning algorithms are used to recognize repetitive moments and limited activity in security footage and consumer videos, cutting out the boring parts.
3. Learned Dictionary Technology - Learned dictionary technology allows the recognition of events in footage, enabling the creation of more engaging video montages.
Industry Implications
1. Film and Video Production - The film and video production industry can utilize self-editing video technology to streamline the editing process and create more engaging content for viewers.
2. Security and Surveillance - The security and surveillance industry can benefit from the use of machine learning algorithms to automatically cut out the monotonous parts of security footage, saving time and improving analysis efficiency.
3. Entertainment and Media - The entertainment and media industry can incorporate self-editing video technology to improve content creation and deliver more captivating experiences to audiences.