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首页> 外文期刊>Soft computing: A fusion of foundations, methodologies and applications >Video analytics-based intelligent surveillance system for smart buildings
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Video analytics-based intelligent surveillance system for smart buildings

机译：基于视频分析的智能建筑智能监控系统

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The goal of the work is to automate video surveillance. The work holds its importance since the camera surveillance under manual supervision fails occasionally. Face images of authorized users in the building are trained, and for each face image, weight is calculated. When the test face comes into the building, the weight for the test face is calculated and compared with the existing weights. Based on the similarity between them, the person's face is identified. For successful recognition of face across the frames, first face image has to be detected. To handle this task, we propose a hybrid algorithm using Haar cascade classifier and skin detector. The stand-alone performance of Haar cascade classifier and skin detector is analyzed, and the work discusses the need for hybridization. The proposed approach addresses the challenges in detecting faces such as orientation changes, varying illumination and partial occlusion. The performance of the system is comparatively analyzed with the videos of frontal and pose-varying faces, and the detection of face across frames is measured. From the experimental analysis, we infer that the detection rate of the proposed hybrid algorithm is 100% for frontal face video and 99.895% for pose-varying face video. The proposed hybrid algorithm is tested with VISOR dataset, and the proposed algorithm achieves precision of 95.20%. We also propose a deep learning framework based on the hybrid algorithm to detect face across the frames. The proposed framework generates more images by affine wrapping strategy and thus handling face orientation changes. The test data are labeled based on counting the prediction results of all the affine transformed images. To evaluate the performance of the system, the framework is tested with frames from VISOR dataset. From the experimental analysis, we infer that the precision of the proposed deep learning framework is 99.10%. Since the person's face has been detected across the maximum number of frames, th
机译：这项工作的目标是自动化视频监控。这项工作拥有其重要性，因为手动监督下的摄像机监控偶尔失败。授权用户在建筑物中的面部图像培训，并且对于每个面部图像，计算重量。当测试面进入建筑物时，计算测试面的重量并与现有权重进行比较。基于它们之间的相似性，确定了人的脸。为了成功地识别跨越框架，必须检测第一面图像。要处理此任务，我们提出了一种使用HAAR级联分类器和皮肤检测器的混合算法。分析了哈尔级联分类器和皮肤探测器的独立性能，工作讨论了对杂交的需求。所提出的方法解决了检测诸如方向变化，不同的照明和部分闭塞等面孔的挑战。用正面和姿势变化面的视频进行比较分析系统的性能，并且测量跨越框架的脸部的检测。从实验分析来看，我们推断所提出的混合算法的检出率为跨越面部视频的100％，姿势变化的面部视频为99.895％。所提出的混合算法用遮阳板数据集进行了测试，所提出的算法实现了95.20％的精度。我们还提出了一种基于混合算法的深度学习框架，以检测框架的面部。所提出的框架通过仿射包装策略产生更多图像，从而处理面向面向变化。基于计数所有仿射器变换图像的预测结果来标记测试数据。为了评估系统的性能，框架用来自Visor DataSet的帧进行测试。从实验分析来看，我们推断提出的深度学习框架的精确度为99.10％。由于在最大帧数中检测到该人的脸，因此

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