Storage and analysis of video surveillance data is a significant challenge, requiring video interpretation and event detection in the relevant context. To perform this task, the low-level features including shape, texture, and color information are extracted and represented in symbolic forms. In this work, a methodology is proposed, which extracts the salient features and properties using machine learning techniques and represent this information as Linked Data using a domain ontology that is explicitly tailored for detection of certain activities. An ontology is also developed to include concepts and properties which may be applicable in the domain of surveillance and its applications. The proposed approach is validated with actual implementation and is thus evaluated by recognizing suspicious activity in an open parking space. The suspicious activity detection is formalized through inference rules and SPARQL queries. Eventually, Semantic Web Technology has proven to be a remarkable toolchain to interpret videos, thus opening novel possibilities for video scene representation, and detection of complex events, without any human involvement. The proposed novel approach can thus have representation of frame-level information of a video in structured representation and perform event detection while reducing storage and enhancing semantically-aided retrieval of video data.

中文翻译：

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使用语义技术的视频表示和可疑事件检测

视频监视数据的存储和分析是一项重大挑战，需要在相关情况下进行视频解释和事件检测。为了执行此任务，包括形状，纹理和颜色信息的低级特征被提取并以符号形式表示。在这项工作中，提出了一种方法，该方法使用机器学习技术提取显着的特征和属性，并使用为检测某些活动而专门设计的领域本体将该信息表示为链接数据。还开发了一种本体，以包括可应用于监视及其应用领域的概念和属性。所提出的方法已通过实际实施进行了验证，因此通过识别空旷的停车位中的可疑活动对其进行了评估。可疑活动检测通过推理规则和SPARQL查询形式化。最终，语义网技术已被证明是解释视频的出色工具链，从而为视频场景表示和复杂事件的检测提供了新颖的可能性，而无需任何人工干预。因此，所提出的新颖方法可以以结构化表示来表示视频的帧级信息，并且在减少存储并增强视频数据的语义辅助检索的同时执行事件检测。