An Improved Lightweight Intrusion Detection Algorithm for Surveillance Video and Its Application
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摘要: 由于传统的目标检测算法较为复杂,在算力、存储空间有限的场景下无法实时检测,因此本文提出了一种轻量级入侵检测算法。首先采用自适应更新率的混合高斯前景提取算法提取初筛目标,然后基于改进的残差压缩网络(R-SqueezeNet)对初筛目标进行识别分类。实验结果表明,该算法在不降低检测精度的前提下,比传统算法的检测速度平均提升了30倍,模型体积缩减至YOLOv3-tiny算法的1/40。
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关键词:
- 监控视频 /
- 入侵检测 /
- 轻量级 /
- 自适应更新率 /
- R-SqueezeNet
Abstract: With the development of target detection algorithms, the intrusion detection based on surveillance video has attracted more attention. Due to the complexity of the traditional target detection algorithm and the difficulty in detecting in real time in the scene of limited computing power and storage space, this paper proposes a lightweight intrusion detection algorithm. Firstly, the preliminary screening target is extracted through the adaptive update rate of the mixed Gaussian foreground extraction algorithm. And then, the preliminary screening target is identified based on the improved residual squeeze network (R-SqueezeNet) classification. It is shown via experimental results that, without reducing the detection accuracy, the proposed algorithm can increase the detection speed by an average of 30 times compared with the traditional algorithm, and the model size is reduced to 1/40 of YOLOv3-tiny.-
Key words:
- surveillance video /
- intrusion detection /
- lightweight /
- adaptive update rate /
- R-SqueezeNet
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表 1 分类网络模型对比
Table 1. Comparison of classification network models
Model Number of Fire module Accuracy/% Inference time/ms Size/MB This paper Cats vs dogs Cifar-10 SqueezeNet 1 90.56 89.9 91.6 1.34 0.16 2 94.35 90.61 93.85 1.7 0.33 3 96.32 92.96 95.74 2.3 0.89 4 96.58 93.17 96.13 3.78 1.5 5 96.67 93.39 96.37 4.04 2.8 R-SqueezeNet 3 96.55 93.2 96.1 2.88 0.89 4 96.73 93.32 96.35 3.92 1.5 5 96.8 93.49 96.55 4.56 2.8 6 97.01 93.56 96.61 7.02 4.1 7 97.2 93.71 96.69 7.68 6.4 8 97.38 93.87 96.73 9.3 8.9 表 2 基于自适应和非自适应前景提取的算法对比
Table 2. Comparison of algorithms based on adaptive and non-adaptive foreground extraction
Based Backbone FD/% MD/% Non-adaptive extraction LeNet 13.8 8.2 AlexNet 9.1 3.3 ZFNet 8.7 2.9 R-SqueezeNet 8.6 2.7 Adaptive extraction LeNet 10.3 8.2 AlexNet 5.4 3.3 ZFNet 5.1 2.9 R-SqueezeNet 4.9 2.7 表 3 本文算法和传统目标检测算法对比
Table 3. Comparison with traditional object detection algorithm
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