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Target detection method based on structural type Haar and Adaboost

A target detection, structured technology, applied in the field of target detection, can solve the problems of the accuracy of the classifier and the weight, and achieve the effect of reducing the amount of calculation, improving the speed, and improving the accuracy of the result.

Inactive Publication Date: 2017-07-21
NANJING UNIV OF SCI & TECH
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AI Technical Summary

Problems solved by technology

However, in the Adaboost training process, each iteration will weight the misclassified samples. When this sample is misclassified many times, the weight will be too large and the accuracy of the classifier will decrease.

Method used

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  • Target detection method based on structural type Haar and Adaboost
  • Target detection method based on structural type Haar and Adaboost
  • Target detection method based on structural type Haar and Adaboost

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Embodiment 1

[0086] Such as figure 1 As shown, first create a sample, obtain the positive sample vector description file and the negative sample description file, and then construct the structural Haar feature, according to the structural Haar feature and the basic Haar features in 5, such as Figure 4 , Figure 5 and Figure 6 As shown, the weak classifier is obtained by training, the strong classifier is obtained by using the weak classifier, and then the cascade classifier is obtained, and finally the target detection is performed by using the obtained cascade classifier, and the final detection result is as follows: Figure 8 shown.

[0087]This algorithm uses the openCV3.0.0 computer vision library developed by Intel Corporation to process various image processing algorithms used in the target detection stage, such as grayscale of color images, image replication, image background detection, etc. The hardware platform of the experiment is: the computer system is Windows 7, 64-bit op...

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Abstract

The invention discloses a target detection method based on structural type Haar and Adaboost. The method includes following steps: creating a sample, and obtaining a positive example sample vector description file and a counter example sample description file; then creating a structural type Haar characteristic, and performing training according to the structural type Haar characteristic to obtain a weak classifier and a strong classifier; and obtaining a cascaded classifier, and finally performing target detection by employing the obtained cascaded classifier to obtain a final detection result. According to the target detection result obtained by the method, good detection precision can be guaranteed, the false detection rate is reduced, the training time is effectively reduced, and the method can be applied to the fields of intelligent traffic detection, video monitoring, and image identification and search etc.

Description

technical field [0001] The invention relates to the technical field of target detection, in particular to a target detection method based on structural Haar and Adaboost. Background technique [0002] Intelligent video surveillance is an important field of computer vision and has a wide range of application scenarios, especially those that are sensitive to security requirements, such as banks, shops, airports, subway stations, parking lots, etc.; in addition, there are industrial production sites Monitoring and monitoring of traffic systems, etc. Intelligent video surveillance can conduct trajectory analysis, behavior recognition and understanding through continuous tracking of targets, and make judgments on whether abnormal events have occurred, so as to take necessary measures and send out alarm signals, and record relevant information at the same time. [0003] At present, the more mature target detection algorithms can be divided into the following three categories: ...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06K9/46
CPCG06V10/44G06V2201/07G06F18/211G06F18/24G06F18/214
Inventor 刘磊邓裕彬刘乾坤李业飞张壮
Owner NANJING UNIV OF SCI & TECH
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