Multi-target tracking method by taking depth as leading clue under dynamic scene

A multi-target tracking and dynamic scene technology, which is applied in image data processing, instrumentation, computing, etc., can solve the problems of limited mining of depth information advantages and no clear role of depth information

Inactive Publication Date: 2012-08-01
XIAMEN UNIV
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AI Technical Summary

Problems solved by technology

This heuristic method does not clarify the role of depth informatio

Method used

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  • Multi-target tracking method by taking depth as leading clue under dynamic scene
  • Multi-target tracking method by taking depth as leading clue under dynamic scene
  • Multi-target tracking method by taking depth as leading clue under dynamic scene

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Embodiment Construction

[0057] The present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments.

[0058] see figure 1 , the implementation of the embodiment of the present invention includes the following steps:

[0059] S1. Use an external trigger source to trigger the binocular camera to simultaneously collect image sequence frames at time t, and then transfer the image data to the computer cache in real time. The binocular camera can work in most environments because it only takes color information, and can be built on a stationary or mobile platform. It should be noted that since the depth algorithm requires the binocular images to have a large overlapping area for the stereo matching algorithm to find corresponding points, there needs to be a relatively small distance between the binocular cameras and they should be placed in parallel.

[0060] S2. After obtaining the current binocular image, correct the binocular image according to...

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Abstract

The invention discloses a multi-target tracking method by taking the depth as a leading clue under a dynamic scene, and relates to a multi-target tracking method. The multi-target tracking method comprises the following steps of: A, acquiring the current frame from a binocular camera, and correcting a binocular image; B, classifying colors of the image, and thus obtaining color blocks; C, performing three-dimensional reconstruction on the scene, and thus obtaining a dense depth picture; D, clustering depth information in an expanded area of a target region of the former frame to obtain a depth classification; E, classifying super pixels in the region and partitioning a foreground from a background according to the depth classification obtained in the step D; F, partitioning the foreground extracted in the step E according to an appearance model of the former frame, and extracting a final foreground; G, if a tracker is in a shielded state, stepping into the step I, and if the tracker is not shielded, returning to the step A, otherwise, recording a target appearance model of the former frame by the tracker, and stepping into the step H; H, extracting a depth block which shields an original target, and setting the tracker to be in the shielded state; and I, searching the original target around the current shielded block, if the original target is found, resetting the tracker to be in a normal state, and returning to the step A.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and relates to a multi-target tracking method, in particular to a multi-target tracking method with depth as the leading clue in a dynamic scene. Background technique [0002] Computer vision is a science that studies how to make machines "see". To put it further, it refers to using cameras and computers instead of human eyes to identify, track and measure targets, and further processing graphics to achieve computer vision. Comprehend what you see and form a high-level perception. [0003] In the field of target tracking, the method adopted for a long time is based on the color appearance model, by looking for the most similar color block to the target appearance (color, shape, outline, etc.) near the target area in the previous frame. The target tracking method based on Meanshift (D. Comaniciu and P. Meer. Mean shift A robust approach toward feature space analysis. IEEE Trans. PAMI, 200...

Claims

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Application Information

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IPC IPC(8): G06T7/20
Inventor 王菡子李弛唐建宇吕乐格雷戈里·海格
Owner XIAMEN UNIV
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