Anti-interference recognition using 3D convolutional network with improved attention block

Jincheng Han; Zhengrong Zuo

doi:10.1117/12.2539241

14 February 2020 Anti-interference recognition using 3D convolutional network with improved attention block

Jincheng Han, Zhengrong Zuo

Proceedings Volume 11430, MIPPR 2019: Pattern Recognition and Computer Vision; 1143014 (2020) https://doi.org/10.1117/12.2539241
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China

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.

Citation Download Citation

Jincheng Han and Zhengrong Zuo "Anti-interference recognition using 3D convolutional network with improved attention block", Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 1143014 (14 February 2020); https://doi.org/10.1117/12.2539241

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KEYWORDS

Target recognition

Infrared radiation

Convolution

Infrared imaging

Detection and tracking algorithms

Target detection

Feature extraction

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