Discriminative Multi-View Dynamic Image Fusion for Cross-View 3-D Action Recognition

判别式 联营 计算机科学 人工智能 模式识别(心理学) 特征(语言学) 编码 代表(政治) 观点 动作识别 杠杆(统计) 特征向量 班级(哲学) 艺术 哲学 语言学 生物化学 化学 政治 政治学 法学 视觉艺术 基因
作者
Yancheng Wang,Yang Xiao,Junyi Lu,Bo Tan,Zhiguo Cao,Zhenjun Zhang,Joey Tianyi Zhou
出处
期刊:IEEE transactions on neural networks and learning systems [Institute of Electrical and Electronics Engineers]
卷期号:33 (10): 5332-5345 被引量:17
标识
DOI:10.1109/tnnls.2021.3070179
摘要

Dramatic imaging viewpoint variation is the critical challenge toward action recognition for depth video. To address this, one feasible way is to enhance view-tolerance of visual feature, while still maintaining strong discriminative capacity. Multi-view dynamic image (MVDI) is the most recently proposed 3-D action representation manner that is able to compactly encode human motion information and 3-D visual clue well. However, it is still view-sensitive. To leverage its performance, a discriminative MVDI fusion method is proposed by us via multi-instance learning (MIL). Specifically, the dynamic images (DIs) from different observation viewpoints are regarded as the instances for 3-D action characterization. After being encoded using Fisher vector (FV), they are then aggregated by sum-pooling to yield the representative 3-D action signature. Our insight is that viewpoint aggregation helps to enhance view-tolerance. And, FV can map the raw DI feature to the higher dimensional feature space to promote the discriminative power. Meanwhile, a discriminative viewpoint instance discovery method is also proposed to discard the viewpoint instances unfavorable for action characterization. The wide-range experiments on five data sets demonstrate that our proposition can significantly enhance the performance of cross-view 3-D action recognition. And, it is also applicable to cross-view 3-D object recognition. The source code is available at https://github.com/3huo/ActionView.
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