Abstract: Embodiments are disclosed for using natural language processing (NLP) to manage security video data. A method of using NLP to search security video data includes receiving, by a surveillance video query system, a text query. A query embedding corresponding to the text query is obtained using a text query model. One or more matching frame embeddings that match the query embedding are identified in a vector database. Matching surveillance video data corresponding to the one or more matching frame embeddings is then obtained from a surveillance video data store. The matching surveillance video data is returned in response to receipt of the text query.

Abstract: Embodiments are disclosed for real-time event detection using edge and cloud AI. An event monitoring system can receive live video data from one or more video capture devices at a surveillance location. A first machine learning model identifies a first portion of the live video data as depicting an event. The first portion of the live video data is provided to a second machine learning model. The second machine learning model identifies the first portion of the live video data as depicting the event. An event notification corresponding to the event is then sent to a user device.

Abstract: Embodiments are disclosed for using natural language processing (NLP) to manage security video data. A method of using NLP to search security video data includes receiving, by a surveillance video query system, a text query. A query embedding corresponding to the text query is obtained using a text query model. One or more matching frame embeddings that match the query embedding are identified in a vector database. Matching surveillance video data corresponding to the one or more matching frame embeddings is then obtained from a surveillance video data store. The matching surveillance video data is returned in response to receipt of the text query.