NDLI: Action recognition with trajectory-pooled deep-convolutional descriptors

Content Provider	IEEE Xplore Digital Library
Author	Limin Wang Yu Qiao Xiaoou Tang
Copyright Year	2015
Description	Author affiliation: Shenzhen Key Lab. of Comp. Vis. & Pat. Rec., Shenzhen Inst. of Adv. Technol., Shenzhen, China (Yu Qiao) \|\| Dept. of Inf. Eng., Chinese Univ. of Hong Kong, Hong Kong, China (Limin Wang; Xiaoou Tang)
Abstract	Visual features are of vital importance for human action understanding in videos. This paper presents a new video representation, called trajectory-pooled deep-convolutional descriptor (TDD), which shares the merits of both hand-crafted features [31] and deep-learned features [24]. Specifically, we utilize deep architectures to learn discriminative convolutional feature maps, and conduct trajectory-constrained pooling to aggregate these convolutional features into effective descriptors. To enhance the robustness of TDDs, we design two normalization methods to transform convolutional feature maps, namely spatiotemporal normalization and channel normalization. The advantages of our features come from (i) TDDs are automatically learned and contain high discriminative capacity compared with those hand-crafted features; (ii) TDDs take account of the intrinsic characteristics of temporal dimension and introduce the strategies of trajectory-constrained sampling and pooling for aggregating deep-learned features. We conduct experiments on two challenging datasets: HMD-B51 and UCF101. Experimental results show that TDDs outperform previous hand-crafted features [31] and deep-learned features [24]. Our method also achieves superior performance to the state of the art on these datasets.
Starting Page	4305
Ending Page	4314
File Size	560945
Page Count	10
File Format	PDF
ISSN	10636919
e-ISBN	9781467369640
DOI	10.1109/CVPR.2015.7299059
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Trajectory Feature extraction Videos Optical imaging Spatiotemporal phenomena Visualization Three-dimensional displays
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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