一种基于空-时快速鲁棒特征的视频词汇的人行为识别方法

Action Recognition Based on Video Words with a Space-Time Speeded up Robust Features Descriptor

提出了一种空-时快速鲁棒特征(SURF)描述子,并且结合视频词汇概念,应用于人行为识别.这种新的描述子在行为识别应用中能很好地体现视频的时空本质,通过词袋(Bag of Words)模型来表征视频,且在表征过程使用了非硬性权重.实验以瑞典皇家理工学院的行为识别数据集作为测试对象,使用了相关领域传统的分类策略,同时引入了包含二次判断的投票系统.实验结果证明,结合特征描述子和视频词汇的行为识别框架在速度和准确率上均优于已有的一些方法,同时该分类策略在某些行为类型上优于传统的分类方法,能有效地应用于行为识别领域.

A novel space-time speeded up robust features（SURF） descriptor and its application to human action recognition by combining with a bag of video words approach were presented.The new descriptor can better represent the spatio-temporal nature of the video data in the application of action recognition.A bag of words approach is used to represent videos,and a soft weighting strategy is exploited.Experiment is done in the KTH＇s action recognition dataset.In the experiment a voting system containing second pass prediction is employed in classifying actions as well as the traditional classification framework.The results of experiment show this approach is able to outperform the previously proposed schema both in speed and accuracy,while the new voting schema works better than the traditional one in some actions.