Video Object Retrieval Using Text-Triggered Cropped Images
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Solution Overview
Problem
Existing video surveillance systems lack interactivity, fail to generate cropped images or alerts based on user inputs, and do not integrate text data for object detection and classification.
Innovation Solution
Implement a compute device configured to detect objects using neural networks, generate cropped images, and trigger alerts based on user-provided text inputs, utilizing joint training of image and language embedding models to align video and text data representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If video surveillance systems process and store all video frames for object detection, then object detection capability is improved, but system resource consumption and storage requirements increase
Solution Approach 1:
The system extracts only the necessary portions of video data by generating cropped images that contain only the detected object and its immediate surroundings, rather than storing or processing entire video frames. This extraction principle reduces storage requirements while maintaining object detection capability.
Solution Approach 2:
The video frames are segmented into relevant and irrelevant portions. The system identifies objects of interest and extracts only those segments (cropped images) for storage and further processing, separating the useful information from the redundant background data.
2Measurement precision
If video surveillance systems generate cropped images for every detected object, then object identification accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary object detection on video frames to identify objects of interest before generating cropped images. This preliminary action allows the system to prepare crop regions in advance based on detected object positions, reducing processing time when actual cropped images need to be generated for storage or retrieval.
3Ease of operation
If video surveillance systems implement text-based search and alert generation, then system interactivity and usability are improved, but system complexity increases
Solution Approach 1:
The system introduces text descriptions as an intermediary between user intent and video data processing. Users provide text descriptions of objects or events, and the system translates these text inputs into detection queries, enabling intuitive interaction without requiring users to understand complex video processing parameters.
Data Source
AI summary
A method for generating and selecting images of objects based on video data and text data includes receiving, at a processor of a video camera system, a video stream including a series of video frames depicting at least one object. A set of at least one classification for the object is generated. Additionally, an image that depicts the object and that includes a cropped portion of a video frame from the series of video frames is generated. A set of at least one index key is generated based on the set of at least one classification, and the image is stored based on the set of at least one index key. The processor receives a signal representing a text input from a user, and the processor performs at least one of (1) retrieval of the image or (2) generation of an alert.