Bus type identifying method

Abstract

The invention discloses a bus type identifying method, belonging to the technical field of processing a computer video. The bus type identifying method comprises the following steps of: carrying out mixed Gaussian modeling on a monitoring video, and carrying out bus type initial identification on a vehicle; secondly, establishing a corresponding 3D (three-dimensional) model according to the position information of the vehicle and the characteristics of a bus; meanwhile, extracting a characteristic segment by using an LSD (Large Screen Display) segment extracting algorithm; and finally, matching the 3D model of the vehicle and the segment characteristics by using a comprehensive algorithm of combining template matching and shortest distance matching. According to the bus type identifying method provided by the invention, the 3D model is established by using the vehicle position information and the bus type characteristics, so that the preparation work of a car type database can be avoided; meanwhile, the vehicle to be judged is initially judged by using the bus type characteristics, so that the 3D modeling and matching process can be carried out on all vehicles in the scene, and the calculation amount is reduced. Finally, the calculation accuracy is improved, and the calculation amount can be reduced by using a combined matching algorithm.

Description

technical field [0001] The invention belongs to the technical field of computer video processing, and specifically relates to a method for identifying a bus type, in particular to designing a method for identifying a type of bus suitable for the application of public security bayonet monitoring. Background technique [0002] At present, vehicle type recognition technology is playing an increasingly important role in public security monitoring, and buses, as an important urban transportation tool, are the key monitoring objects in monitoring. [0003] Feature point matching method and 3D model matching method are two common vehicle detection methods. However, the feature point matching method usually needs to match all the extracted 2D features (such as edge line segments, edge pixels, etc.) with the 2D features of the model (see: Grimson, W., "The combinatorics of heuristic search termination for object recognition in cluttered environment," IEEE Trans.PAMI., vol.13, no.9, ...

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Problems solved by technology

However, the feature point matching method usually needs to match all the extracted 2D features (such as edge line segments, edge pixels, etc.) with the 2D features of the model (see: Grimson, W., "The combinatorics of heuristic search termination for object recognition in cluttered environment," IEEE Trans.PAMI., vol.13, no.9, pp.920-935, 1991.) Therefore, it has a large amount of calculation and relatively poor real-time performance, so it cannot be directly applied to the real-time monitoring system of the bayonet

Traditional 3D matching methods (see: (See: Tan, T.N., Sullivan, G.D., Baker, K.D., "Model-based localization and recognition of road vehicles," Int. J. Comput. Vis., vol. 27, no. 1, pp.5-25, 1998.) Its recognition accuracy depends on the completeness of the 3D model database. However, it is difficult for most application equipment to have a complete 3D vehicle model database, and the complexity of the method will increase with the number of models. Linear increase, so it is difficult to directly apply to bayonet monitoring equipment

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