Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Vision-based research for intelligent vehicles have traditionally focused on specific regions around a vehicle, such as a front looking camera for, e.g., lane estimation. Traffic scenes are complex and vital information could be lost in unobserved regions.  proposes a framework that uses four visual sensors for a full surround view of a vehicle in order to achieve an understanding of surrounding vehicle behaviors. The framework will assist the analysis of naturalistic driving studies by automating the task of data reduction of the observed trajectories. To this end, trajectories are estimated using a vehicle detector together with a multiperspective optimized tracker in each view. The trajectories are transformed to a common ground plane, where they are associated beten perspectives and analyzed to reveal tendencies around the ego-vehicle. The system is tested on sequences from 2.5 h of drive on US highways. The multiperspective tracker is tested in each view as ll as for the ability to associate vehicles bet-en views with a 92% recall score. A case study of vehicles approaching from the rear shows certain patterns in behavior that could potentially influence the ego-vehicle.
['Jacob Velling Dueholm', 'Miklas Strøm Kristoffersen', 'Ravi Kumar Satzoda', 'Thomas B. Moeslund', 'Mohan M. Trivedi']
I am writing a research paper on a idea Trajectories and Maneuvers of Surrounding Vehicles With Panoramic Camera Arrays
[{"from": "human", "value": "I am writing a research paper on a idea Trajectories and Maneuvers of Surrounding Vehicles With Panoramic Camera Arrays"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Vision-based research for intelligent vehicles have traditionally focused on specific regions around a vehicle, such as a front looking camera for, e.g., lane estimation. Traffic scenes are complex and vital information could be lost in unobserved regions.  proposes a framework that uses four visual sensors for a full surround view of a vehicle in order to achieve an understanding of surrounding vehicle behaviors. The framework will assist the analysis of naturalistic driving studies by automating the task of data reduction of the observed trajectories. To this end, trajectories are estimated using a vehicle detector together with a multiperspective optimized tracker in each view. The trajectories are transformed to a common ground plane, where they are associated beten perspectives and analyzed to reveal tendencies around the ego-vehicle. The system is tested on sequences from 2.5\u00a0h of drive on US highways. The multiperspective tracker is tested in each view as ll as for the ability to associate vehicles bet-en views with a 92% recall score. A case study of vehicles approaching from the rear shows certain patterns in behavior that could potentially influence the ego-vehicle."}]