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Anomalies in Surveillance Video

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  • Dangerous Object Detection and Tracking using YOLO: While anomaly detection in surveillance video is effective for identifying unusual behaviors such as violence, vandalism, abduction, or trespassing, etc., it may not always trigger alerts based solely on the presence of dangerous objects. However, the appearance of weapons, firearms, or other objects that could be used in crimes serves as a crucial indicator of potential danger. This project focuses on fine-tuning the well-known YOLO object detector to identify dangerous objects. This would help enhance surveillance capabilities by identifying potential threats at an earlier stage.
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  • Video Indexing for Rapid Surveillance Footage Summarization (VIDEX, with GMDSOFT): Alongside anomaly detection and object detection methods, we are developing applications to make the analysis of surveillance footage more efficient. This project involves creating a video indexing system that allows the results of anomaly and object detection to be stored, searched, and navigated easily. The goal is to assist investigators in maximizing their efficiency when analyzing large volumes of video data by providing quick access to critical segments.
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  • Driving Habit Analysis using Dashcam Videos (with AI-CAR): In some countries including Korea, dashcams are widely used to monitor the surroundings of vehicles. While sensor-based traffic signal violation detection and ADAS (Advanced Driver Assistance Systems)-based lane departure warnings are already familiar, this project focuses on monitoring safe driving, rather than law enforcement. To achieve this, we have developed a Raspberry Pi-based dashcam with wireless connectivity for real time data collection. The system records and analyzes metrics such as lane departure frequency and traffic signal violations, to eventually provide a safety score for drivers based on their behavior.
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  • Driving Habit Analysis using Dashcam Videos (with AI-CAR): In some countries including Korea, dashcams are widely used to monitor the surroundings of vehicles. While sensor-based traffic signal violation detection and ADAS (Advanced Driver Assistance Systems)-based lane departure warnings are already familiar, this project focuses on monitoring safe driving, rather than law enforcement. To achieve this, we have developed a Raspberry Pi-based dashcam with wireless connectivity for real time data collection. The system records and analyzes metrics such as lane departure frequency and traffic signal violations, to eventually provide a safety score for drivers based on their behavior. + +
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