Motorcycle type identifying method in complex scene

A vehicle type identification and complex scene technology, applied in the field of vehicle type identification, can solve problems such as model failure, vehicles are divided into three types, large, medium and small, and it is difficult to select parts, so as to overcome the influence of image noise, ensure real-time performance and versatility, The effect of extending the range of application

Inactive Publication Date: 2012-08-22
湖北莲花山计算机视觉和信息科学研究院
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Problems solved by technology

Although such methods are more robust than prototype-based methods, they also have common shortcomings: first, such methods are still very dependent on the quality of image segmentation, and often only deal with the background Simple situation; second, the features selected by this type of method are not robust enough; third, the models used by this type of method are relatively simple and can only represent rough information of the target, and generally can only be divided into large, medium and small Three types, and cannot be further classified in detail; Fourth, this type of method is still highly dependent

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  • Motorcycle type identifying method in complex scene

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Embodiment Construction

[0037] The technical terms of the present invention are firstly explained and illustrated below.

[0038] Component: corresponds to a part of the vehicle, which may be a wheel, window, door, or an area on the body, etc.;

[0039] Parts dictionary: a collection of all parts;

[0040] Positive sample: It consists of two parts: the image containing the vehicle and the position of the vehicle in the image (marked with the coordinates of the upper left corner and the lower right corner of the rectangular box).

[0041] Negative samples: images that do not contain vehicles.

[0042] Learning samples: Positive and negative samples.

[0043] And-or search tree: A concept in artificial intelligence and computer vision, which is generalized by and-or graph, and-or graph is a method of systematically decomposing a problem into independent small problems and then solving them separately. There are two representative nodes in an AND-or graph: "AND node" and "OR node". "And node" means ...

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Abstract

The invention discloses a motorcycle type identifying method in a complex scene, which comprises the following steps of: initializing a component dictionary of a video image, learning a parameter of each component in the component dictionary, calculating an optimal composition structure according to the learned parameters of the components and an XOR searching tree, training and integrating vehicle templates by adopting the optimal composition structure, and detecting and identifying a motorcycle type in the video image by using the vehicle templates. According to the motorcycle type identifying method, the optimal composition structure of the vehicle templates is learned by adopting a dynamic planning algorithm, the XOR searching tree and a large quantity of actual samples, thus the efficiency of training the templates is increased, better discrimination is achieved, and actual application is facilitated. According to the motorcycle type identifying method, by combining a Latent SVM (Support Vector Machine) algorithm and a robust HOG (Histograms of Oriented Gradients) characteristic, the motorcycle type in the complex scene can be processed, and instantaneity and generality are ensured.

Description

technical field [0001] The invention relates to the fields of image pattern recognition, intelligent video monitoring and intelligent transportation, in particular to a method for vehicle vehicle recognition in complex scenes. Background technique [0002] Vehicle type recognition based on video images refers to automatically identifying different types of vehicles from images and videos, such as vans, cars, large trucks, buses, etc. It is a key technology in intelligent transportation systems, whether in the field of intelligent traffic monitoring , or in the field of fully automatic toll collection of highways and parking lots, it has extremely important applications. [0003] Car recognition based on video images is generally divided into three parts: 1. Segmentation of vehicle images; 2. Feature extraction; 3. Recognition and classification of car models. The relevant vehicle identification methods in the current literature mainly include: (a) prototype-based vehicle id...

Claims

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Application Information

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IPC IPC(8): G06K9/66G08G1/017
Inventor 朱松纯李博姚振宇
Owner 湖北莲花山计算机视觉和信息科学研究院
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