Target detection method and device

Abstract

The invention provides a target detection method and a target detection device. The method comprises the steps of learning a codebook based on an initial training sample set, establishing a visual word network model based on the codebook, extracting the word frequency histogram feature of each sample in the current training sample set, obtaining the weight of each visual word through network adjustment operation, determining the kernel function of a k nearest neighbor classifier according to the weights, determining the k nearest neighbor classifier, detecting an area to be detected according to the k nearest neighbor classifier, and thereby judging whether the area to be detected is the target. If the detection risk is not completed, after the training sample is obtained on line, the training sample set is updated, the visual word weights are updated by network operation, the kernel function is adjusted according to the weights, and then the area to be detected is detected. During the process, the k nearest neighbor classifier can self-adjust according to the current situation, the adaptation of the k nearest neighbor classifier on the current target and background appearance rule is maintained, and thereby the reliable and real-time target detection under the variable scene is achieved.

Description

technical field [0001] The invention relates to image detection technology, in particular to a target detection method and device. Background technique [0002] Object detection refers to the technology of identifying and locating objects of interest in images or image sequences. This technology is based on computer vision and is widely used in military, civilian and other fields. [0003] The detection algorithm used in target detection technology is also called a detector. From a structural point of view, the detector includes a feature extraction module and a feature classification module. Among them, the feature extraction module is used to abstractly describe the appearance of the area to be detected as "features" according to certain rules, and the feature classification module is used to classify the area to be detected as a certain type of target or background according to the features extracted by the feature extraction module . In the target detection process, d...

AI Technical Summary

Problems solved by technology

The change of the detection scene makes the originally designed detector not suitable for the target detection in the changed detection scene, resulting in low detection accuracy

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