Target object detection method and device

A technology of target objects and detection methods, which is applied in the field of target detection, can solve problems such as increased storage space, and achieve the effect of improving efficiency and fast detection speed

Active Publication Date: 2017-12-19
BEIJING XIAOMI MOBILE SOFTWARE CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

When the number of slides increases or the window becomes larger, the storage space required for the tracking process will rise sharply; and since there will be overlapping areas between adjacent sliding windows, a lot of useless work has been done in the tracking process

Method used

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  • Target object detection method and device
  • Target object detection method and device
  • Target object detection method and device

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Embodiment Construction

[0066] Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numerals in different drawings refer to the same or similar elements unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatuses and methods consistent with aspects of the present disclosure as recited in the appended claims.

[0067] figure 1 is a flow chart of a method for detecting a target object according to an exemplary embodiment, as shown in figure 1 As shown, the method can be applied to terminals such as mobile phones, tablet computers, and cameras. The detection methods of this target object include:

[0068] Step S101. Detect the first position of the target object in the current frame image of...

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Abstract

The invention relates to a target object detection method and device. The method comprises the steps that the first position of a target object in the current frame image of a to-be-processed video is detected; in a next frame image of the current frame image, an enlargement area is determined according to the first position, wherein the enlargement area is an area occupying a predetermined area in the next frame image, and comprises the first position; and the enlargement area is input into a full convolution network FCN model to acquire the second position of the target object in the enlargement area. According to the invention, the FCN model is used for target detection; since the enlargement area input into the FCN model has a smaller range than the entire frame image input into a CNN model, and has fewer repeated areas than the CNN model which uses a sliding window for target detection, the detection speed is faster; and the efficiency of target object detection is improved.

Description

technical field [0001] The present disclosure relates to the field of target detection, in particular to a method and device for detecting a target object. Background technique [0002] In the related technologies, as the research on the artificial neural network becomes more and more in-depth, the deep learning method based on the artificial neural network has been successfully applied in many fields. For example: in the field of computer vision, in the field of speech recognition, and in the field of object tracking. This disclosure further studies the application of deep learning methods in the field of target detection. [0003] When detecting a target object, a CNN (Convolutional Neural Network, Convolutional Neural Network) model is usually trained by an online training method, and the CNN model is used to detect the target object and then track the target object. Specifically, a sliding window method is used in the surrounding area where the target object is located...

Claims

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Application Information

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IPC IPC(8): G06T7/246G06T7/277G06N3/04G06N3/08
CPCG06T7/248G06T7/277G06N3/08G06T2207/20076G06T2207/20081G06T2207/20084G06T2207/20021G06T2207/10016G06N3/045
Inventor 陈志军
Owner BEIJING XIAOMI MOBILE SOFTWARE CO LTD
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