Global feature and local feature-based cross-lens target retrieval system and method

A technology of global features and local features, applied in the field of cross-lens target retrieval system, can solve the problems of various camera types and models, large collection workload, matrix differences, etc., to reduce the number of traversals, ensure real-time performance, and improve accuracy. Effect

Inactive Publication Date: 2017-07-07
BEIJING DATANG GOHIGH DATA NETWORKS TECH CO LTD
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Problems solved by technology

The problem with this method is that it is necessary to collect a large number of original images of the same target from different cameras as samples, the collection workload is large, there are many types and models of cameras, and it is difficult to realize, and the two target images that need to be matched

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  • Global feature and local feature-based cross-lens target retrieval system and method
  • Global feature and local feature-based cross-lens target retrieval system and method
  • Global feature and local feature-based cross-lens target retrieval system and method

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Embodiment Construction

[0052] The present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments.

[0053] figure 1This is the flow chart of the method for establishing a cross-border head target retrieval system of the present invention. As shown in the figure, the cross-shot target retrieval system based on global features and local features disclosed in the present invention is established as follows:

[0054] S10: Obtain a foreground target image including an active target from surveillance videos of multiple cameras;

[0055] The monitoring scene is modeled by the mixed Gaussian model, and a background image and several foreground mask images are extracted from each frame of the monitoring video. The description of the foreground mask image is:

[0056]

[0057] where p mask (x, y) is the pixel value of the foreground mask image at point (x, y), p f (x, y) is the pixel point of the video frame at (x, y).

[0058] The foreground m...

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Abstract

The invention discloses a global feature and local feature-based cross-lens target retrieval system and method. The method comprises the steps of obtaining a foreground target image from a monitoring video; preprocessing the foreground target image, and extracting a color feature, a texture feature and the like with a plurality of feature dimensions from the image; and after dimension reduction processing, storing the features in a database, so that the target retrieval system is formed. When the target retrieval system is used for retrieving a query image, firstly feature information of the query image is extracted, and the query image and the foreground target image in the target system are subjected to global feature similarity calculation to obtain a primary screening result; and secondly local feature information of the query image is extracted, the query image and the foreground target image subjected to primary screening are subjected to local feature similarity calculation, and the foreground target image with high global and local feature similarity is taken as a query result image. According to the system and the method, the identification accuracy and performance of cross-lens target retrieval can be improved.

Description

technical field [0001] The invention relates to a cross-shot target retrieval system and method based on global features and local features, belonging to the technical field of image processing and pattern recognition. Background technique [0002] With the development of video surveillance technology, in order to achieve the efficiency of retrieving objects from massive surveillance videos, cross-camera object retrieval technology emerges as the times require. Query the target, greatly shorten the time to find the original video, and improve the efficiency and recognition accuracy. There are two main methods for cross-border head target retrieval: [0003] One is to extract a variety of features with stability (consistent features at different times) and distinctiveness (inconsistent features at the same time or different times) from the original image, and splicing multiple features in series to form the feature vector of the target image. . The problem with this method...

Claims

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/785G06F16/7857
Inventor 付景林侯玉成赵德胜王芊刘雪峰丁明锋张新中鞠秀芳王允升赵志诚杨永强李鹏
Owner BEIJING DATANG GOHIGH DATA NETWORKS TECH CO LTD
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