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Action recognition based on efficient deep feature learning in the spatio-temporal domain

AuthorsHusain, Farzad ; Dellen, Babette ; Torras, Carme
KeywordsComputer vision for automation
Visual learning
Issue Date2016
PublisherInstitute of Electrical and Electronics Engineers
CitationIEEE Robotics and Automation Letters 1(2): 984-991 (2016)
AbstractHand-crafted feature functions are usually designed based on the domain knowledge of a presumably controlled environment and often fail to generalize, as the statistics of real-world data cannot always be modeled correctly. Data-driven feature learning methods, on the other hand, have emerged as an alternative that often generalize better in uncontrolled environments. We present a simple, yet robust, 2-D convolutional neural network extended to a concatenated 3-D network that learns to extract features from the spatio-temporal domain of raw video data. The resulting network model is used for content-based recognition of videos. Relying on a 2-D convolutional neural network allows us to exploit a pretrained network as a descriptor that yielded the best results on the largest and challenging ILSVRC-2014 dataset. Experimental results on commonly used benchmarking video datasets demonstrate that our results are state-of-the-art in terms of accuracy and computational time without requiring any preprocessing (e.g., optic flow) or a priori knowledge on data capture (e.g., camera motion estimation), which makes it more general and flexible than other approaches. Our implementation is made available.
Publisher version (URL)https://doi.org/10.1109/LRA.2016.2529686
Identifiersdoi: 10.1109/LRA.2016.2529686
issn: 2377-3766
Appears in Collections:(IRII) Artículos
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