In this study, the authors present a new approach to segment and classify moving objects in video sequences by combining an unsupervised anomaly discovery framework called DeepSphere and generative adversarial networks. The proposed deep detector classifier employs and validates DeepSphere, which aims mainly to identify the anomalous cases in the spatial and temporal context in order to perform foreground objects segmentation. For post-processing, some morphological operations are considered to better segment and extract the desired objects. Finally, they take advantage of the power of generative models, which recognise the problem of semi-supervised learning as a specific missing data imputation task in order to classify the segmented objects. They evaluate the method with multiple datasets and the results confirm the effectiveness of the proposed approach, which achieves superior performance over the state-of-the-art methods having the capabilities of segmenting and classifying moving objects from videos surveillance.

中文翻译：

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深度检测器分类器（DeepDC），用于视频监控中的运动对象分割和分类

在这项研究中，作者提出了一种新方法，通过结合称为DeepSphere的无监督异常发现框架和生成对抗网络，对视频序列中的运动对象进行分割和分类。提出的深度探测器分类器采用并验证了DeepSphere，它的主要目的是识别时空上下文中的异常情况，以执行前景对象分割。对于后处理，可以考虑使用某些形态学操作来更好地分割和提取所需的对象。最后，他们利用生成模型的功能，该模型将半监督学习的问题识别为特定的缺失数据插补任务，以便对分割的对象进行分类。