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.
It is a nontrivial task to manage crowds in public places and recognize unacceptable behavior (such as violating social distancing norms during the COVID-19 pandemic). In such situations, people should avoid loitering (unnecessary moving out in public places without apparent purpose) and maintain a sufficient physical distance. In this study, a multi-object tracking algorithm has been introduced to improve short-term object occlusion, detection errors, and identity switches. The objects are tracked through bounding box detection and with linear velocity estimation of the object using the Kalman filter frame by frame. The predicted tracks are kept alive for some time, handling the missing detections and short-term object occlusion. ID switches (mainly due to crossing trajectories) are managed by explicitly considering the motion direction of the objects in real time. Furthermore, a novel approach to detect unusual behavior of loitering with a severity level is proposed based on the tracking information. An adaptive algorithm is also proposed to detect physical distance violation based on the object dimensions for the entire length of the track. At last, a mathematical approach to calculate actual physical distance is proposed by using the height of a human as a reference object which adheres more specific distancing norms. The proposed approach is evaluated in traffic and pedestrian movement scenarios. The experimental results demonstrate a significant improvement in the results.
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