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Proceedings Paper

Anti-interference recognition using 3D convolutional network with improved attention block
Author(s): Jincheng Han; Zhengrong Zuo
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Paper Abstract

The discrimination of interferences, especially artificial interferences, such as decoy, is crucial to improving the target’s detection performance. The differences in the kinematics characteristics between the target and the decoys are the main foundation to classify targets and kinds of decoys. The kinematics characteristics of the target and decoy usually represented by their behavior patterns. In this paper, learned from the human behavior recognition methods, a method for infrared target and decoy recognition based on the behavior recognition network was proposed. Our method combines detection network (Faster-RCNN), association algorithm (Deep-Sort), Inflated 3D convolutional network (I3D) with long-range attention block to perform target and decoy behavior recognition. Interactions with surrounding objects and other objects contain important information towards understanding the behavior. Improving the non-local attention mechanism by aggregating channel-wise attention and trajectory attention, our proposal method enables the I3D network to efficiently capture relation features on any positions, time and channels, especially trajectory behavior features of the target and decoy, that improve the discriminative ability of the anti-interference behavior recognition network. Experiments show our proposed method has a better performance than the original non-local attention network, achieve a state-of-the-art.

Paper Details

Date Published: 14 February 2020
PDF: 7 pages
Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 1143014 (14 February 2020); doi: 10.1117/12.2539241
Show Author Affiliations
Jincheng Han, Huazhong Univ. of Science and Technology (China)
Zhengrong Zuo, Huazhong Univ. of Science and Technology (China)

Published in SPIE Proceedings Vol. 11430:
MIPPR 2019: Pattern Recognition and Computer Vision
Nong Sang; Jayaram K. Udupa; Yuehuan Wang; Zhenbing Liu, Editor(s)

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