Cost-Effective Vehicle Type Recognition in Surveillance Images with Deep Active Learning and Web Data
Product Description
Abstract:
With the rapid development of Intelligent Transportation System (ITS) technologies in recent years, vehicle type recognition (VTR) has been widely applied as a low-cost technology in tolls, transportation statistics, security and crime prevention issues. The early techniques for VTR mainly relied on the detection and recognition of vehicle license plates and logos. In vehicle type recognition, the shape and appearance of vehicles can also be considered to be other features, such as size silhouette dimension and aspect ratio.
A deep active learning framework for vehicle type recognition is proposed in order to dramatically reduce the burden of large-scale annotation in surveillance data. In the proposed system To overcome the above shortcomings in traditional vehicle identification systems, automated VMMR (Vehicle Make and Model Recognition) techniques have recently gained attention, but without considering processing speed as the primary factor. In this way, automated VMMR systems augment traditional license plate recognition-based vehicle identification systems to further enhance security. Unexplored approaches for VMMR are proposed and evaluated based on the BoSURF framework, in which the dominant features of all makes and models are learned and represented in an optimized dictionary.
Proposed System:
To overcome the above shortcomings in traditional vehicle identification systems, automated VMMR (Vehicle Make and Model Recognition) techniques have recently gained attention, but without considering processing speed as the primary factor. The make and model of the vehicle recognized by the VMMR system can be crosschecked with the license plate registry to screen for fraud. In this way, automated VMMR systems augment traditional license plate recognition-based vehicle identification systems to further enhance security. Unexplored approaches for VMMR are proposed and evaluated based on the BoSURF framework, in which the dominant features of all makes and models are learned and represented in an optimized dictionary.
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The Delivery time for Hardware Mini projects is 7-8 working days.