DeepAct: A Deep Neural Network Model for Activity Detection in Untrimmed Videos


Yeongtaek Song, Incheol Kim, Journal of Information Processing Systems Vol. 14, No. 1, pp. 150-161, Jan. 2018  

10.3745/JIPS.04.0059
Keywords: Activity Detection, Bi-directional LSTM, Deep Neural Networks, Untrimmed Video
Fulltext:

Abstract

We propose a novel deep neural network model for detecting human activities in untrimmed videos. The process of human activity detection in a video involves two steps: a step to extract features that are effective in recognizing human activities in a long untrimmed video, followed by a step to detect human activities from those extracted features. To extract the rich features from video segments that could express unique patterns for each activity, we employ two different convolutional neural network models, C3D and I-ResNet. For detecting human activities from the sequence of extracted feature vectors, we use BLSTM, a bi-directional recurrent neural network model. By conducting experiments with ActivityNet 200, a large-scale benchmark dataset, we show the high performance of the proposed DeepAct model.


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Cite this article
[APA Style]
Yeongtaek Song and Incheol Kim (2018). DeepAct: A Deep Neural Network Model for Activity Detection in Untrimmed Videos. Journal of Information Processing Systems, 14(1), 150-161. DOI: 10.3745/JIPS.04.0059.

[IEEE Style]
Y. Song and I. Kim, "DeepAct: A Deep Neural Network Model for Activity Detection in Untrimmed Videos," Journal of Information Processing Systems, vol. 14, no. 1, pp. 150-161, 2018. DOI: 10.3745/JIPS.04.0059.

[ACM Style]
Yeongtaek Song and Incheol Kim. 2018. DeepAct: A Deep Neural Network Model for Activity Detection in Untrimmed Videos. Journal of Information Processing Systems, 14, 1, (2018), 150-161. DOI: 10.3745/JIPS.04.0059.