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首页> 外文期刊>Arabian Journal for Science and Engineering >Efficient and Fast Objects Detection Technique for Intelligent Video Surveillance Using Transfer Learning and Fine-Tuning
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Efficient and Fast Objects Detection Technique for Intelligent Video Surveillance Using Transfer Learning and Fine-Tuning

机译：利用转移学习和微调的智能视频监控高效快速目标检测技术

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摘要

Intelligent video surveillance systems require effective techniques in order to detect objects accurately and rapidly. The mostsuitable algorithms for performing this task are based on convolutional neural networks. Existing approaches encounter awide range of difficulties in terms of dealing with different sizes, high definition, or colored images turning these latterslower and less precise. The real-time sensitive application offers an interesting challenge for the optimization of the qualityand quantity of previous approaches, thus obtaining an efficient system with regard to surveillance environment. This paperpresents a novel, fast, and precise technique for advanced object detection as far as intelligent video surveillance systemsare concerned. Thus, we propose the transfer learning of an efficient pre-trained network to appropriate datasets for ourapplication and its integration in the architecture of our algorithm. Accordingly, we implement a fine-tuning on this pretrainedmodel via replacing the softmax layer and running backpropagation. Then, we compare the results of the previousalgorithms using common evaluation parameters. The experimental results reveal that with this technique, we can enhancethe precision and the accuracy of object detection in video surveillance scenes to more than 90%. Furthermore, along withdealing with different input dimensions, the detector runs in real time. To conclude, our application of machine learning forintelligent video surveillance systems maximizes their efficiency in highly difficult situations.
机译：智能视频监控系统需要有效的技术，以便准确，快速地检测物体。最适合执行此任务的算法基于卷积神经网络。现有的方法在处理不同的尺寸，高清晰度或彩色图像方面遇到了各种各样的困难，使这些方法变得更慢，更不精确。实时灵敏的应用程序为优化先前方法的质量和数量提出了一个有趣的挑战，从而在监视环境方面获得了一个有效的系统。就智能视频监控系统而言，本文提出了一种用于高级目标检测的新颖，快速，精确的技术。因此，我们提出将有效的预训练网络转移学习到适合我们的应用程序的适当数据集，并将其集成到算法的体系结构中。因此，我们通过替换softmax层并运行向后传播，对该预训练模型进行微调。然后，我们使用共同的评估参数比较先前算法的结果。实验结果表明，采用该技术可以将视频监控场景中的目标检测精度和检测精度提高到90％以上。此外，随着处理不同的输入尺寸，检测器可以实时运行。总而言之，我们在智能视频监控系统的机器学习中的应用在极端困难的情况下将其效率最大化。

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