基于单类SVM和加权多示例采样方法的目标跟踪算法

Journal of Southwest China Normal University（2014）

Cited 23|Views7

Abstract

基于分类的跟踪算法成为当前目标跟踪的研究热点。首先把跟踪问题看成是一个目标和背景的二分类问题，根据每一帧的正负样本数据训练 SVM分类器，通过分类器的分类概率值确定目标位置。然而，采集正负样本边界的那些样本很容易出现异常点，当把它们作为目标的下一帧位置时将会出现严重的跟踪漂移问题。本文在此基础上提出一种基于单类支持向量机(One-class support vector machine)的目标跟踪算法，基于 One-class SVM分类能有效地排除其他类的干扰，有效地防止异常样本的出现。并结合加权多示例采样方法，使得每个采样样本会根据不同的权值对于分类器的贡献而不同。实验结果表明本文改进跟踪方法的鲁棒性。

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Key words

binary classification,obj ect tracking,one-class SVM,weighted multi-instance sampling method

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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