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Target tracking method and system based on partitioned multi-example learning algorithm

A multi-instance learning and target tracking technology, which is applied in the field of target tracking methods and systems based on the block multi-instance learning algorithm, can solve the problems of tracking result drift, target tracking performance is not high enough, and MIL algorithm calculation is time-consuming. The effect of stable target tracking and improved performance

Active Publication Date: 2016-09-28
HEBEI COLLEGE OF IND & TECH
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Problems solved by technology

[0003] However, the MIL algorithm takes a lot of time to calculate, is prone to tracking result drift, and the target tracking performance is not high enough, and it cannot solve serious problems such as illumination, pose changes, and occlusions.

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  • Target tracking method and system based on partitioned multi-example learning algorithm
  • Target tracking method and system based on partitioned multi-example learning algorithm
  • Target tracking method and system based on partitioned multi-example learning algorithm

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Embodiment Construction

[0036] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0037] The basic idea of ​​existing multi-instance learning algorithms is to implement multi-instance learning algorithms under the framework of Online Boosting. The algorithm collects examples in the target and background areas respectively to form marked packets as training samples, namely: {(X 1 ,y 1 ),…,(X i ,y i ),…,(X m ,y m )}. Among them, X i ={x i1 ,x i2 ,,x in} for example {x i1 ,x i2 ,,x in} consisting of packets, y i mark for the package. when y i = 1, the package is a positive package, when y i =0, the packet is a negative packet.

[0038] The positive packet consists of samples collected within the domain of the target location in the current frame: in is the current position of the target, and r is the radius of the circle where the sample is collected. Collect examples at the background to form a ...

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Abstract

The invention discloses a target tracking method based on a partitioned multi-example learning algorithm. The method includes that a target image is divided into a plurality of partitioned image sheets; the weak classifier pool of each partitioned image sheet is obtained through the multi-example learning algorithm, and a plurality of weak classifiers having high classification capability are selected from the weak classifier pool to form strong classifiers; and during the target tracking, the score of an integrated classifier of the target image is calculated through the combination with the strong classifiers of all of the partitioned image sheets, and a target position is determined based on the score of the integrated classifier. The target tracking method based on the partitioned multi-example learning algorithm is high in tracking performance and stable in tracking process, and can solve the problems of serious lighting and pose change and shielding. The invention further discloses a target tracking system based on a partitioned multi-example learning algorithm.

Description

technical field [0001] The invention relates to the technical field of digital image processing, in particular to an object tracking method and system based on block multi-instance learning algorithm. Background technique [0002] Object tracking technology is one of the topics of much concern in the field of machine vision. In recent years, many scholars at home and abroad have devoted themselves to the research of target tracking technology and achieved some remarkable results. However, object tracking still faces many challenges, such as: noise, illumination, pose changes, motion mutations, and occlusions. To solve the above problems, Babenko proposed a target tracking method based on the Multiple Instance Learning (MIL) algorithm. The method represents samples as labeled bags (positive or negative) consisting of multiple examples. A bag is marked as positive when at least one example in it is positive. Conversely, when all examples in a bag are negative, the bag is m...

Claims

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Application Information

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IPC IPC(8): G06T7/20G06K9/62
CPCG06T2207/20081G06T2207/20021G06F18/285G06F18/2155G06F18/24
Inventor 王振杰李月朋杨朝晖梁海军
Owner HEBEI COLLEGE OF IND & TECH
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