基于提高的稠密轨迹人体行为识别

doi:10.16182/j.issn1004731x.joss.201709023

摘要/Abstract

摘要： 由于稠密轨迹对快速不规则运动的鲁棒性较强,近年来基于稠密轨迹的人体运动表征方法越来越多地运用于行为识别等领域。但是由于相机运动造成背景相对运动,对轨迹的提取产生了很大的影响。加入了相机运动估计,为了估计相机运动,用快速鲁棒特征描述符匹配每一帧的特征点。由于人的运动和相机运动不一样,加上了人体检测去除不一致的匹配。通过多示例学习对交互行为进行分类识别。并在UT-Interaction 数据集上进行了测试,证明了该方法的有效性。

关键词: 稠密轨迹, 相机运动估计, 人体检测, 多示例学习

Abstract: In recent years, with strong robustness to fast irregular motion, the method of human motion representation based on dense trajectories has been used more and more in the field of behavior recognition. However, the relative motion of the background caused by the motion of the camera has a great influence on the extraction of the trajectories. In order to estimate the camera motion, the speed up robust feature (SURF) descriptor was used to match the feature points of each frame. Since the human motion and the camera motion were not same, human detection was added to remove inconsistent matches. Finally, multi instance learning (MIL) was used to classify and recognize the behavior. Experiment results demonstrate the effectiveness of the approach on the UT-interaction dataset.

Key words: dense trajectories, the estimate of camera motion, human detection, multi instance learning

中图分类号:

TP391

许培振, 余志斌, 金炜东, 蒋海英. 基于提高的稠密轨迹人体行为识别[J]. 系统仿真学报, 2017, 29(9): 2053-2058.

Xu Peizhen, Yu Zhibin, Jin Weidong, Jiang Haiying. Action Recognition by Improved Dense Trajectories[J]. Journal of System Simulation, 2017, 29(9): 2053-2058.

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