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WatchNet++: efficient and accurate depth-based network for detecting people attacks and intrusion
Machine Vision and Applications ( IF 3.3 ) Pub Date : 2020-06-17 , DOI: 10.1007/s00138-020-01089-y
M. Villamizar , A. Martínez-González , O. Canévet , J.-M. Odobez

We present an efficient and accurate people detection approach based on deep learning to detect people attacks and intrusion in video surveillance scenarios Unlike other approaches using background segmentation and pre-processing techniques, which are not able to distinguish people from other elements in the scene, we propose WatchNet++ that is a depth-based and sequential network that localizes people in top-view depth images by predicting human body joints and pairwise connections (links) such as head and shoulders. WatchNet++ comprises a set of prediction stages and up-sampling operations that progressively refine the predictions of joints and links, leading to more accurate localization results. In order to train the network with varied and abundant data, we also present a large synthetic dataset of depth images with human models that is used to pre-train the network model. Subsequently, domain adaptation to real data is done via fine-tuning using a real dataset of depth images with people performing attacks and intrusion. An extensive evaluation of the proposed approach is conducted for the detection of attacks in airlocks and the counting of people in indoors and outdoors, showing high detection scores and efficiency. The network runs at 10 and 28 FPS using CPU and GPU, respectively.

中文翻译:

WatchNet ++:高效,准确的基于深度的网络,用于检测人员攻击和入侵

我们提供一种基于深度学习的有效,准确的人员检测方法,以检测视频监视场景中的人员攻击和入侵。与其他使用背景分割和预处理技术的方法不同,这些方法无法将人员与场景中的其他元素区分开来,提出了WatchNet ++,它是一个基于深度的顺序网络,可以通过预测人体的关节和头对肩等成对连接(链接)来将人定位在顶视图深度图像中。WatchNet ++包含一组预测阶段和上采样操作,它们逐渐完善关节和链接的预测,从而获得更准确的定位结果。为了用各种丰富的数据训练网络,我们还展示了具有人体模型的深度图像的大型综合数据集,该数据集用于预训练网络模型。随后,通过使用人员进行攻击和入侵的深度图像的真实数据集的微调,可以完成对实际数据的域适应。对提议的方法进行了广泛的评估,以检测气闸中的袭击并统计室内和室外人员的数量,显示出较高的检测分数和效率。该网络分别使用CPU和GPU以10 FPS和28 FPS的速度运行。对提议的方法进行了广泛的评估,以检测气闸中的袭击并统计室内和室外人员的数量,显示出较高的检测分数和效率。该网络分别使用CPU和GPU以10 FPS和28 FPS的速度运行。对提议的方法进行了广泛的评估,以检测气闸中的袭击并统计室内和室外人员的数量,显示出较高的检测分数和效率。该网络分别使用CPU和GPU以10 FPS和28 FPS的速度运行。
更新日期:2020-06-17
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