An Hybrid CPU-GPU Parallel Multi-tracking Framework for Long-Term Video Sequences

The automatic evaluation of video content is today one of the biggest challenges in computer Vision. When the purpose is to work with static surveillance cameras, where most of the time the scenes do not change, a full Convolutional Network (CNN) approach seems to require too much CPU effort, specially when the objects are slightly moving between different frames. On the other side, visual tracking has seen great recent advances in either speed or accuracy but still remain scarce when have to deal with long videos where new objects constantly come into the scene and others disappear.