Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Humans use context and scene knowledge to easily localize moving objects in conditions of complex illumination changes, scene clutter and occlusions. ,  present a method to leverage human knowledge in the form of annotated video libraries in a novel search and retrieval based setting to track objects in unseen video sequences. For every video sequence, a document that represents motion information is generated. Documents of the unseen video are queried against the library at multiple scales to find videos with similar motion characteristics. This provides us with coarse localization of objects in the unseen video.  further adapt these retrieved object locations to the new video using an efficient warping scheme. The proposed method is validated on in-the-wild video surveillance datasets where  outperform state-of-the-art appearance-based trackers.  also introduce a new challenging dataset with complex object appearance changes.
['Archith John Bency', 'S. Karthikeyan', 'Carter De Leo', 'Santhoshkumar Sunderrajan', 'B. S. Manjunath']
I am writing a research paper on a idea Search Tracker: Human-derived object tracking in-the-wild through large-scale search and retrieval
[{"from": "human", "value": "I am writing a research paper on a idea Search Tracker: Human-derived object tracking in-the-wild through large-scale search and retrieval"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Humans use context and scene knowledge to easily localize moving objects in conditions of complex illumination changes, scene clutter and occlusions. ,  present a method to leverage human knowledge in the form of annotated video libraries in a novel search and retrieval based setting to track objects in unseen video sequences. For every video sequence, a document that represents motion information is generated. Documents of the unseen video are queried against the library at multiple scales to find videos with similar motion characteristics. This provides us with coarse localization of objects in the unseen video.  further adapt these retrieved object locations to the new video using an efficient warping scheme. The proposed method is validated on in-the-wild video surveillance datasets where  outperform state-of-the-art appearance-based trackers.  also introduce a new challenging dataset with complex object appearance changes."}]