Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  address the problem of automatic extraction of foreground objects from videos. The goal is to provide a method for unsupervised collection of samples which can be further used for object detection training without any human intervention.  use the ll known Selective Search approach to produce an initial still-image based segmentation of the video frames. This initial set of proposals is pruned and temporally extended using optical flow and transductive learning. Specifically,  propose to use Dense Trajectories in order to robustly match and track candidate boxes over different frames. The obtained box tracks are used to collect samples for unsupervised training of track-specific detectors. Finally, the detectors are run on the videos to extract the final tubes. The combination of appearance-based static "objectness" (Selective Search), motion information (Dense Trajectories) and transductive learning (detectors are forced to "overfit" on the unsupervised data used for training) makes the proposed approach extremely robust.  outperform state-of-the-art systems by a large margin on common benchmarks used for tube proposal evaluation.
['Mihai Marian Puscas', 'Enver Sangineto', 'Dubravko Culibrk', 'Nicu Sebe']
I am writing a research paper on a idea Unsupervised Tube Extraction Using Transductive Learning and Dense Trajectories
[{"from": "human", "value": "I am writing a research paper on a idea Unsupervised Tube Extraction Using Transductive Learning and Dense Trajectories"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  address the problem of automatic extraction of foreground objects from videos. The goal is to provide a method for unsupervised collection of samples which can be further used for object detection training without any human intervention.  use the ll known Selective Search approach to produce an initial still-image based segmentation of the video frames. This initial set of proposals is pruned and temporally extended using optical flow and transductive learning. Specifically,  propose to use Dense Trajectories in order to robustly match and track candidate boxes over different frames. The obtained box tracks are used to collect samples for unsupervised training of track-specific detectors. Finally, the detectors are run on the videos to extract the final tubes. The combination of appearance-based static \"objectness\" (Selective Search), motion information (Dense Trajectories) and transductive learning (detectors are forced to \"overfit\" on the unsupervised data used for training) makes the proposed approach extremely robust.  outperform state-of-the-art systems by a large margin on common benchmarks used for tube proposal evaluation."}]