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Action prediction method based on multi-task random forest

A random forest and action prediction technology, applied in the field of computer vision, can solve problems such as long time-consuming and low accuracy

Active Publication Date: 2019-12-31
SHENYANG AEROSPACE UNIVERSITY
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Problems solved by technology

[0004] In view of this, the object of the present invention is to provide a multi-task random forest-based action prediction method, to at least solve the problems that existing action prediction methods take a long time and have low accuracy for incomplete video prediction action categories

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  • Action prediction method based on multi-task random forest
  • Action prediction method based on multi-task random forest
  • Action prediction method based on multi-task random forest

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Embodiment Construction

[0053] The present invention will be further explained below in conjunction with specific embodiments, but the present invention is not limited thereto.

[0054] The present invention provides a kind of action prediction method based on multi-task random forest, comprises the following steps:

[0055] S1: Use training data to build an action prediction model based on multi-task random forest, where multi-task random forest is an integrated learning model containing N multi-task decision trees,

[0056] Among them, the steps to construct a multi-task random forest are as follows:

[0057] S11: Collect a training set containing M incomplete videos Each sample in the training set D contains the feature vector x m ∈R F×1 , action category label Observation rate label Incomplete videos of , where F represents the number of features in the feature vector, K represents the number of action categories; specifically, first collect a set of complete videos with action category l...

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Abstract

The invention discloses an action prediction method based on a multitask random forest, and the method comprises the following steps: building an action prediction model based on the multitask randomforest through employing a training video marked with an action type label and an observation rate label at the same time; for a newly input video containing incomplete actions, predicting the actiontype of the video by using a multi-task random forest. According to the action prediction method based on the multi-task random forest, aiming at the difficulties that an input video is incomplete andthe observation rate is unknown in action prediction, the performance of an action prediction model is greatly improved by jointly learning classifiers of two tasks, namely action classification andvideo observation rate identification.

Description

technical field [0001] The invention relates to the field of computer vision, and in particular provides an action prediction method based on a multi-task random forest. Background technique [0002] Computer vision is a field of study that uses geometry, physics, and learning theory to build models and use statistical methods to process data. Action recognition is to discriminate the action based on the complete video. However, in life, it is often necessary to respond to the action before the action is completed, that is, before the complete video is observed. Therefore, people began to study how to predict the incomplete video. The category of the action video. Human action prediction mainly includes two steps, action representation and prediction of human action category. Action representation refers to extracting information such as appearance, motion, and structure from the input video to generate feature vectors that describe the video. Action prediction establishe...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V20/46G06F18/24323G06F18/214
Inventor 刘翠微于天宇杜冲石祥滨李照奎
Owner SHENYANG AEROSPACE UNIVERSITY
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