Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present an approach for object segmentation in videos that combines frame-level object detection with concepts from object tracking and motion segmentation. The approach extracts temporally consistent object tubes based on an off-the-shelf detector. Besides the class label for each tube, this provides a location prior that is independent of motion. For the final video segmentation,  combine this information with motion cues. The method overcomes the typical problems of akly supervised/unsupervised video segmentation, such as scenes with no motion, dominant camera motion, and objects that move as a unit. In contrast to most tracking methods, it provides an accurate, temporally consistent segmentation of each object.  report results on four video segmentation datasets: YouTube Objects, SegTrackv2, egoMotion, and FBMS.
['Benjamin Drayer', 'Thomas Brox']
I am writing a research paper on a idea Object Detection, Tracking, and Motion Segmentation for Object-level Video Segmentation
[{"from": "human", "value": "I am writing a research paper on a idea Object Detection, Tracking, and Motion Segmentation for Object-level Video Segmentation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present an approach for object segmentation in videos that combines frame-level object detection with concepts from object tracking and motion segmentation. The approach extracts temporally consistent object tubes based on an off-the-shelf detector. Besides the class label for each tube, this provides a location prior that is independent of motion. For the final video segmentation,  combine this information with motion cues. The method overcomes the typical problems of akly supervised/unsupervised video segmentation, such as scenes with no motion, dominant camera motion, and objects that move as a unit. In contrast to most tracking methods, it provides an accurate, temporally consistent segmentation of each object.  report results on four video segmentation datasets: YouTube Objects, SegTrackv2, egoMotion, and FBMS."}]