Woven Planet North America, Inc.

Coram Ai, Inc.

Qiangui Huang

Peter Ondruska

Meng Gao

Ashesh Jain

Yawei Ye

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.

Natural language processing for searching security video data

A method includes accessing a training sample including an image of a scene, depth measurements of the scene, and a predetermined 3D position of an object in the scene. The method includes training a 3D-detection model for detecting 3D positions of objects based the depth measurements and the predetermined 3D position, and training a 2D-detection model for detecting 2D positions of objects within images. Training the 2D-detection model includes generating an estimated 2D position of the object by processing the image using the 2D-detection model, determining a subset of the depth measurements that correspond to the object based on the estimated 2D position and a viewpoint from which the image is captured, generating an estimated 3D position of the object based on the subset of the depth measurements, and updating the 2D-detection model based on a comparison between the estimated 3D position and the predetermined 3D position.

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Palo Alto, CA, United States of America

Qiangui Huang