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Characteristic optimization method based on coevolution for foot passenger detection

A co-evolutionary, pedestrian detection technology, applied in the field of pedestrian detection, can solve the problems of inability to obtain a reasonable proportion of features, weak classification ability, etc., to avoid premature convergence, facilitate encoding and decoding, and ensure stability.

Inactive Publication Date: 2008-08-20
UNIV OF SCI & TECH OF CHINA
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Problems solved by technology

The advantage of this type of method is that it can find a part of feature combinations that meet the performance requirements from the specified feature set in a short period of time; but it also cannot get a reasonable proportion of features, and some redundant features and classification capabilities will also be selected. some features of

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  • Characteristic optimization method based on coevolution for foot passenger detection
  • Characteristic optimization method based on coevolution for foot passenger detection

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Embodiment Construction

[0026] As shown in Figure 1, the present invention comprises the following steps:

[0027] (1) Read in training samples, including positive samples (images containing pedestrians) and negative samples (images without pedestrians), and all samples are scaled to a uniform specification.

[0028] All samples are taken from real-time video and manually intercepted from each frame of the video. The positive samples contain a complete pedestrian, and the negative samples contain pedestrian-like objects, such as trees, roadblocks, etc. Each sample is a 24-bit bitmap scaled to a uniform specification: 16 pixels × 32 pixels.

[0029] (2) Generate the original feature set. Since color information is the key to distinguishing pedestrians, it is necessary to extract features from the R, G, B channel and grayscale images respectively to obtain four types of eigenvalues ​​of R, G, B, and GRAY. Each category has n i , i=1, 2, 3, 4 features, a total of features, the eigenvalues ​​of eac...

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Abstract

The invention relates to a feature optimization selection method for a pedestrian detection based on coevolution, which includes that: (1) a training sample is read in; (2) an original characteristic set is generated and a sample set is formed; (3) four populations are initialized and a type of characteristic is corresponded to each population; (4)an individual is decoded to a feature combination and then a new sample subset is obtained, and fitness of the individual is calculated; (5) a terminal condition is judged for whether the requirement is met, if the terminal condition is met, a characteristic subset denoted by a best individual in each population is used as the optimum relation of an algorithm; (6) a competition within the population, an inter-population competition and self-increase rules are used for choosing the individual according to the fitness of each individual, a method for single interior extrapolation and its variation are used for generating the next generation individual; (7) the (4) step is returned and the population is evolved until an feature selection terminal condition of the (5) step is satisfied. The invention decreases the complexity of computation, and can obtain an optimizing feature subset, and promotes the veracity for pedestrian classification.

Description

technical field [0001] The invention relates to a pedestrian detection method in the field of intelligent transportation, in particular to a feature optimization method based on co-evolution for pedestrian detection. Background technique [0002] In recent years, my country's road traffic accidents have shown a rapid growth trend, of which urban traffic accidents account for the main part. In view of the characteristics of complex scenes, numerous pedestrians and vulnerability in urban traffic, pedestrian safety protection is the key to urban traffic safety. For this reason, Pedestrian Detection System (PDS: Pedestrian Detection System) has become a key technology of great concern to the research and industry circles. [0003] Classification detection is currently the mainstream technology for pedestrian detection systems. In order to achieve accurate and fast pedestrian classification and detection, it is necessary to select as many types of features as possible; in recen...

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Application Information

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IPC IPC(8): G06K9/62
Inventor 曹先彬许言午郭圆平魏闯先吴培嘉晓岚
Owner UNIV OF SCI & TECH OF CHINA
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