Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Vehicle detection and annotation for streaming video data with complex scenes is an interesting but challenging task for urban traffic surveillance. ,  present a fast framework of Detection and Annotation for Vehicles (DAVE), which effectively combines vehicle detection and attributes annotation. DAVE consists of two convolutional neural networks (CNNs): a fast vehicle proposal network (FVPN) for vehicle-like objects extraction and an attributes learning network (ALN) aiming to verify each proposal and infer each vehicle’s pose, color and type simultaneously. These two nets are jointly optimized so that abundant latent knowledge learned from the ALN can be exploited to guide FVPN training. Once the system is trained, it can achieve efficient vehicle detection and annotation for real-world traffic surveillance data.  evaluate DAVE on a new self-collected UTS dataset and the public PASCAL VOC2007 car and LISA 2010 datasets, with consistent improvements over existing algorithms.
['Yi Zhou', 'Li Liu', 'Ling Shao', 'Matt Mellor']
I am writing a research paper on a idea DAVE: A Unified Framework for Fast Vehicle Detection and Annotation
[{"from": "human", "value": "I am writing a research paper on a idea DAVE: A Unified Framework for Fast Vehicle Detection and Annotation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Vehicle detection and annotation for streaming video data with complex scenes is an interesting but challenging task for urban traffic surveillance. ,  present a fast framework of Detection and Annotation for Vehicles (DAVE), which effectively combines vehicle detection and attributes annotation. DAVE consists of two convolutional neural networks (CNNs): a fast vehicle proposal network (FVPN) for vehicle-like objects extraction and an attributes learning network (ALN) aiming to verify each proposal and infer each vehicle\u2019s pose, color and type simultaneously. These two nets are jointly optimized so that abundant latent knowledge learned from the ALN can be exploited to guide FVPN training. Once the system is trained, it can achieve efficient vehicle detection and annotation for real-world traffic surveillance data.  evaluate DAVE on a new self-collected UTS dataset and the public PASCAL VOC2007 car and LISA 2010 datasets, with consistent improvements over existing algorithms."}]