Enhanced Action Recognition Using Multiple Stream Deep Learning with Optical Flow and Weighted Sum

Sensors
Hyunwoo KimJoonki Paik

Abstract

Various action recognition approaches have recently been proposed with the aid of three-dimensional (3D) convolution and a multiple stream structure. However, existing methods are sensitive to background and optical flow noise, which prevents from learning the main object in a video frame. Furthermore, they cannot reflect the accuracy of each stream in the process of combining multiple streams. In this paper, we present a novel action recognition method that improves the existing method using optical flow and a multi-stream structure. The proposed method consists of two parts: (i) optical flow enhancement process using image segmentation and (ii) score fusion process by applying weighted sum of the accuracy. The enhancement process can help the network to efficiently analyze the flow information of the main object in the optical flow frame, thereby improving accuracy. A different accuracy of each stream can be reflected to the fused score while using the proposed score fusion method. We achieved an accuracy of 98.2% on UCF-101 and 82.4% on HMDB-51. The proposed method outperformed many state-of-the-art methods without changing the network structure and it is expected to be easily applied to other networks.

References

Dec 18, 2007·IEEE Transactions on Pattern Analysis and Machine Intelligence·Karthik NandakumarAnil K Jain
Jan 30, 2020·IEEE Transactions on Neural Networks and Learning Systems·Xiao-Yu ZhangJing Dong

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Software Mentioned

PoTion
I3D
Resnet
Action CNN
DeepLabV3

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