SecurifAI automatically detects abnormal events by learning a model of normal events from training video.
Abnormal event detection in video is a challenging task in computer vision, as the definition of what an abnormal event looks like depends very much on the context. For instance, a car driving by on the street is regarded as a normal event, but if the car enters a pedestrian area, this is regarded as an abnormal event. A person running on a sports court (normal event) versus running outside from a bank (abnormal event) is another example. Although what is considered abnormal depends on the context, abnormal behaviour should be represented by unexpected events that occur less often than familiar (normal) events.
Hence, our approach to abnormal behavior detection is based on learning a model of familiarity from a given training video and label events as abnormal if they deviate from the model. We employ deep convolutional neural networks and support vector machines to achieve state-of-the-art results for the abnormal event detection task. Our system is flexible and can be adjusted to reach a desirable trade-off between true and false detections.
Our solution is based on the remarkable knowledge and skills acquired by our research and development team in the fields of artificial intelligence, machine learning, computer vision and image processing. Our team brings the next generation of video surveillance software by leveraging the use of recent developments in these fields, including neural networks, deep learning and other state-of-the-art approaches. Incorporating state-of-the-art technology into our software leads to high detection rates of life-threatening situations and events.
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