Abstract
In this paper, our goal is to improve the recognition accuracy of battlefield target aggregation behavior while maintaining the low computational cost of spatio-temporal depth neural networks. To this end, we propose a novel 3D-CNN (3D Convolutional Neural Networks) model, which extends the idea of multi-scale feature fusion to the spatio-temporal domain, and enhances the feature extraction ability of the network by combining feature maps of different convolutional layers. In order to reduce the computational complexity of the network, we further improved the multi-fiber network, and finally established an architecture—3D convolution Two-Stream model based on multi-scale feature fusion. Extensive experimental results on the simulation data show that our network significantly boosts the efficiency of existing convolutional neural networks in the aggregation behavior recognition, achieving the most advanced performance on the dataset constructed in this paper.
Highlights
Battlefield target aggregation behavior is a common group behavior in the joint operations environment, which is usually a precursor to important operational events such as force adjustment, battle assembly, and sudden attack
The intelligence video records the different behaviors of the battlefield targets, and effectively identifying the aggregate behavior in the video is the main purpose of this paper
In order to verify the advantages of this network framework in identifying accuracy and computational complexity, we tested the identification results of the networks in the dataset constructed in this paper
Summary
Battlefield target aggregation behavior is a common group behavior in the joint operations environment, which is usually a precursor to important operational events such as force adjustment, battle assembly, and sudden attack. To grasp the battlefield initiative, it is important to identify the aggregation behavior of enemy targets. The intelligence video records the different behaviors of the battlefield targets, and effectively identifying the aggregate behavior in the video is the main purpose of this paper. The identification of battlefield aggregation behavior requires a manual interpretation, which is inefficient in battlefield environments. It is an inevi trend for intelligent battlefield development to introduce intelligent recognition algorithms to identify the aggregation behavior. 3D Convolutional Neural Networks (3D-CNN), which show significant results in behavior recognition, provide a technical basis for battlefield target aggregation behavior recognition
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