Cascaded Temporal Spatial Features for Video Action Recognition
Tingzhao Yu1,2; Huxiang Gu1; Lingfeng Wang1; Shiming Xiang and1; Chunhong Pan1
2017
会议名称IEEE International Conference on Image Processing
会议日期2017-9-17
会议地点Beijing, CHINA
摘要Extracting spatial-temporal descriptors is a challenging task for video-based human action recognition. We decouple the 3D volume of video frames directly into a cascaded temporal spatial domain via a new convolutional architecture. The motivation behind this design is to achieve deep nonlinear feature representations with reduced network parameters. First, a 1D temporal network with shared parameters is first constructed to map the video sequences along the time axis into feature maps in temporal domain. These feature maps are then organized into channels like those of RGB image (named as Motion Image here for abbreviation), which is desired to preserve both temporal and spatial information. Second, the Motion Image is regarded as the input of the latter cascaded 2D spatial network. With the combination of the 1D temporal network and the 2D spatial network together, the size of whole network parameters is largely reduced. Benefiting from the Motion Image, our network is an end-to-end system for the task of action recognition, which can be trained with the classical algorithm of back propagation. Quantities of comparative experiments on two benchmark datasets demonstrate the effectiveness of our new architecture.
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/20351
专题模式识别国家重点实验室_先进数据分析与学习
作者单位1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
2.School of Computer and Control Engineering, University of Chinese Academy of Sciences
推荐引用方式
GB/T 7714
Tingzhao Yu,Huxiang Gu,Lingfeng Wang,et al. Cascaded Temporal Spatial Features for Video Action Recognition[C],2017.
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