Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Dynamic appearance is one of the most important cues for tracking and identifying moving people. Hover, direct modeling spatio-temporal variations of such appearance is often a difficult problem due to their high dimensionality and nonlinearities.   present a human tracking system that uses a dynamic appearance and motion modeling framework based on the use of robust system dynamics identification and nonlinear dimensionality reduction techniques. The proposed system learns dynamic appearance and motion models from a small set of initial frames and does not require prior knowledge such as gender or type of activity. The advantages of the proposed tracking system are illustrated with several examples where the learned dynamics accurately predict the location and appearance of the targets in future frames, preventing tracking failures due to model drifting, target occlusion and scene clutter.
['Hwasup Lim', 'Octavia I. Camps', 'Mario Sznaier', 'Vlad I. Morariu']
I am writing a research paper on a idea Dynamic Appearance Modeling for Human Tracking
[{"from": "human", "value": "I am writing a research paper on a idea Dynamic Appearance Modeling for Human Tracking"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Dynamic appearance is one of the most important cues for tracking and identifying moving people. Hover, direct modeling spatio-temporal variations of such appearance is often a difficult problem due to their high dimensionality and nonlinearities.   present a human tracking system that uses a dynamic appearance and motion modeling framework based on the use of robust system dynamics identification and nonlinear dimensionality reduction techniques. The proposed system learns dynamic appearance and motion models from a small set of initial frames and does not require prior knowledge such as gender or type of activity. The advantages of the proposed tracking system are illustrated with several examples where the learned dynamics accurately predict the location and appearance of the targets in future frames, preventing tracking failures due to model drifting, target occlusion and scene clutter."}]