Vehicle Video Query Triggers for Custom Event Detection
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Solution Overview
Problem
Current dashcam systems lack flexibility in customizing event triggers for capturing vehicle video data, leading to inefficient resource usage and ineffective identification of relevant events due to static AI models that require extensive reprogramming.
Innovation Solution
A video system that utilizes a large language model (LLM) to generate captions for video frames and a phrase model to categorize words, calculating matching scores based on user-defined queries, enabling dynamic event detection and reducing the need for constant AI model retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static AI models are used for event detection in dashcam systems, then the system structure is simple and stable, but the system lacks flexibility in customizing event triggers and requires extensive reprogramming for new event types
Solution Approach 1:
The patent introduces a query processing module as an intermediary between user input and the video analysis system. This module converts natural language queries into structured event detection parameters, allowing users to customize event triggers without modifying the underlying AI models. The intermediary layer enables flexible customization while keeping the core system structure intact.
Solution Approach 2:
The system segments the event detection functionality into separate query processing and video analysis components. The query processing module handles customization independently from the video analysis module, allowing flexible event trigger configuration without affecting the stability of the core video processing system.
2Adaptability or versatility
If static AI models are used for event detection, then the system is stable and easy to maintain, but extensive reprogramming is required for new event types
Solution Approach 1:
The query processing module serves as an intermediary that translates user-defined natural language queries into event detection parameters. This eliminates the need for reprogramming when new event types are needed - users simply input new queries, and the system automatically adapts without requiring code modifications.
Solution Approach 2:
The system changes the approach from modifying program code to changing query parameters. New event types are detected by adjusting the natural language query inputs rather than reprogramming the AI models, making the system easily adaptable to new event types while maintaining stability.
3Reliability
If static AI models with fixed triggers are used, then the system consumes fewer computing resources, but it captures unnecessary video data and fails to identify relevant events
Solution Approach 1:
The system transitions from static AI models with fixed triggers to a dynamic query-based trigger system. The event detection parameters are dynamically adjusted based on user-defined queries and real-time video content analysis, improving event identification accuracy while optimizing resource usage by focusing computation on relevant events.
Solution Approach 2:
The system implements feedback mechanisms where query processing results and video analysis outcomes inform subsequent event detection decisions. This feedback loop ensures that computing resources are allocated efficiently by prioritizing analysis of potentially relevant events identified through query matching, rather than uniformly processing all video data.
4Reliability
If customizable event triggers are implemented, then event detection accuracy improves, but system complexity and resource requirements increase
Solution Approach 1:
The system segments complexity into manageable modules: query processing, video analysis, and event detection. Each module performs a specific function independently, allowing the system to achieve high event detection accuracy through coordinated modular operations without overwhelming overall system complexity.
Solution Approach 2:
The query processing module serves multiple functions: it parses user queries, generates event detection parameters, and adapts to different event types. This multi-functionality reduces the need for separate specialized components for each event type, managing system complexity while maintaining high detection accuracy across diverse events.
Data Source
AI summary
A device may receive a query identifying one or more events to be captured by a camera associated with a vehicle, video data associated with the vehicle, and location data associated with the vehicle, and may generate a prompt requesting captions of video frames included in the video data. The device may process the video data, with a large language model and based on the prompt, to generate the captions of the video frames included in the video data, and may process the query and the captions, with a phrase model, to determine categories of words provided in the query and the captions. The device may calculate matching scores between the query and the captions based on the categories of words and a dictionary of words, and may identify an event based on the matching scores. The device may perform one or more actions based on the event.