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Vehicle detection and annotation for streaming video data with complex scenesis an interesting but challenging task for urban traffic surveillance. In thispaper, we 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 vehicleproposal network (FVPN) for vehicle-like objects extraction and an attributeslearning network (ALN) aiming to verify each proposal and infer each vehicle'spose, color and type simultaneously. These two nets are jointly optimized sothat abundant latent knowledge learned from the ALN can be exploited to guideFVPN training. Once the system is trained, it can achieve efficient vehicledetection and annotation for real-world traffic surveillance data. We evaluateDAVE on a new self-collected UTS dataset and the public PASCAL VOC2007 car andLISA 2010 datasets, with consistent improvements over existing algorithms.

DAVE: A Unified Framework for Fast Vehicle Detection and Annotation