多视角下的交互行为识别（英文）

Interaction behavior recognition from multiple views

本文提出了一种基于局部自相似描述符和图共享多任务学习的多视角交互行为识别方法。首先,提出了一种复合交互特征表示方法,该方法对兴趣点局部运动的空间分布及其上下文进行编码。其次,为了减小观测角度变化对识别的影响并保留时序信息,用时间金字塔词袋模型对局部自相似描述符进行表示。为了从不同的视角探索不同交互行为之间的潜在关联,并保留每种交互行为的特定信息,采用图共享多任务学习学习相应的交互行为识别模型。结果表明,该方法在CASIA、i3Dpose公共数据集和自建交互行为识别数据库上相比其他方法识别率更高。

This paper proposed a novel multi-view interactive behavior recognition method based on local self-similarity descriptors and graph shared multi-task learning. First, we proposed the composite interactive feature representation which encodes both the spatial distribution of local motion of interest points and their contexts. Furthermore, local self-similarity descriptor represented by temporal-pyramid bag of words(BOW) was applied to decreasing the influence of observation angle change on recognition and retaining the temporal information. For the purpose of exploring latent correlation between different interactive behaviors from different views and retaining specific information of each behaviors, graph shared multi-task learning was used to learn the corresponding interactive behavior recognition model. Experiment results showed the effectiveness of the proposed method in comparison with other state-of-the-art methods on the public databases CASIA, i3Dpose dataset and self-built database for interactive behavior recognition.