Multi-Camera Video Analysis With VLM Token Control
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Solution Overview
Problem
The sheer volume of traffic data from multiple cameras at intersections poses a challenge for timely analysis, as existing video-to-text conversion methods using Vision-Language Models (VLMs) are slow and inefficient, leading to significant latency in processing and analyzing video feeds.
Innovation Solution
A novel algorithm that adjusts the maximum token limit parameter of VLMs and leverages multi-camera setups by employing sophisticated prompt engineering to reduce redundancy and expedite the video-to-text conversion process, using iterative prompts with varying token limits across cameras to capture distinct perspectives and minimize redundant information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If Vision-Language Models (VLMs) are used to convert video feeds to text, then semantic information can be extracted, but processing time increases significantly
Solution Approach 1:
The patent segments the video processing task by identifying and processing non-overlapping frames from multiple camera feeds separately. Each frame is processed independently through the VLM, and results are aggregated. This segmentation reduces the total processing time while maintaining semantic information extraction quality.
Solution Approach 2:
The patent changes key parameters of the VLM processing by adjusting the maximum token limit dynamically based on frame importance and camera priority. It also modifies the processing sequence by prioritizing certain cameras over others, changing the operational parameters to balance information extraction with processing speed.
2Area of stationary object
If multiple camera feeds are processed simultaneously, then comprehensive scene coverage is achieved, but data redundancy increases
Solution Approach 1:
The patent extracts only the essential and non-redundant information from multiple camera feeds. It identifies unique objects and events in each camera's field of view and extracts only those elements that provide new information, discarding duplicate data while maintaining comprehensive scene coverage.
Solution Approach 2:
The patent merges results from multiple camera feeds by aggregating detected objects and events, combining unique information while eliminating duplicates. The system consolidates data from multiple sources into a unified representation, achieving comprehensive coverage without proportionally increasing data volume.
3Measurement precision
If maximum token limit is increased for detailed descriptions, then extraction accuracy improves, but processing speed decreases
Solution Approach 1:
The patent applies partial action by using different maximum token limits for different frames and cameras based on their importance and information content. Critical frames receive higher token limits for detailed extraction, while less important frames use lower limits, achieving overall accuracy without uniformly sacrificing speed.
Solution Approach 2:
The patent dynamically adjusts the maximum token limit parameter based on real-time conditions such as frame uniqueness, object importance, and processing backlog. This dynamic parameter adjustment allows the system to optimize the balance between extraction accuracy and processing speed adaptively throughout the video analysis process.
Data Source
AI summary
Systems and methods for multi-camera video analysis using large language models. Non-overlapping frames can be identified from multiple video feeds from a base camera and secondary cameras. Similar information from the multiple video feeds can be filtered to remove redundancies from the non-overlapping frames from the non-overlapping frames and obtain filtered frames. Textual data that describes semantic information of entities can be extracted from the filtered frames using a vision-language model (VLM). Undetected objects can be identified from the filtered frames by analyzing the textual data and the entities within different perspectives of the filtered frames. Combined textual captions that combines the textual data and descriptions of the undetected objects into embedded vectors can be generated for the multiple video feeds. Corrective action can be performed for a monitored entity based on the combined textual captions from the embedded vectors.