Rear-Facing Scene Assembly for Front-Facing Incident Retrieval
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Solution Overview
Problem
Data collection for public-safety incidents is inefficient and time-consuming, leading to a loss of critical information such as identifying suspects and witnesses, as existing methods struggle with processing large volumes of images from dual-sensor cameras.
Innovation Solution
A computing device assembles rear-facing camera images from dual-sensor cameras into a scene image, allowing for the identification of a region-of-interest, and renders associated front-facing camera images to efficiently identify witnesses and suspects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data collection methods are used to gather information from the general public, then critical data such as images of suspects and witnesses can be collected, but the process becomes time-consuming and inefficient use of processing resources
Solution Approach 1:
The system performs preliminary actions by automatically assembling rear-facing camera images into a scene image before the need to identify specific details arises. This pre-processing creates a comprehensive overview that enables rapid identification of suspects and witnesses without requiring time-consuming manual review of individual images from the general public.
Solution Approach 2:
The scene image serves as an intermediary that synthesizes multiple rear-facing camera images into a unified view. This intermediary structure allows investigators to quickly locate and access relevant front-facing camera images of suspects and witnesses without having to search through all collected data from the general public, thus reducing time loss while preventing information loss.
2Loss of information
If data collection methods are used to gather information from the general public, then critical data can be collected, but processing resources and bandwidth are inefficiently used
Solution Approach 1:
The system segments the data collection and processing task by separating rear-facing camera images (used for assembling the scene image) from front-facing camera images (used for identifying suspects and witnesses). This segmentation allows the system to process only relevant images after the scene image is assembled, improving processing efficiency while maintaining complete critical data collection.
Solution Approach 2:
The system extracts and prioritizes relevant information by using the assembled scene image to identify which front-facing camera images contain suspects or witnesses. This extraction approach allows the system to focus processing resources only on the most relevant images rather than processing all collected data from the general public, thereby improving productivity while preventing information loss.
3Loss of information
If sorting through public-provided images is performed, then relevant data can be found, but the process is challenging and leads to inefficient use of processing resources
Solution Approach 1:
The system merges multiple rear-facing camera images into a single scene image that provides a comprehensive overview of the incident scene. This merging process simplifies the data sorting complexity by creating a unified structure that makes it easier to identify and retrieve relevant front-facing camera images of suspects and witnesses, thereby improving data retrieval accuracy without increasing device complexity.
4Loss of information
If dual-sensor camera images are processed, then both rear-facing and front-facing images are available, but processing time and resource consumption increase
Solution Approach 1:
The system performs preliminary action by assembling rear-facing camera images into a scene image before processing front-facing camera images. This pre-assembly creates a reference framework that enables rapid identification of relevant front-facing images, reducing overall processing time while maintaining information completeness through the dual-sensor camera data.
Solution Approach 2:
The system applies partial action by processing only the necessary portion of dual-sensor camera images. After assembling the scene image from rear-facing images, the system selectively processes only the front-facing images that are relevant to identifying suspects and witnesses, rather than processing all dual-sensor images equally, thus reducing processing time while maintaining information completeness.
Data Source
AI summary
A device, system, and method to provide front-facing camera images identified using a scene image assembled from rear-facing camera images is provided. A device retrieves dual-sensor camera images having respective metadata indicating image acquisitions substantially matching a time and a place associated with an incident, the dual-sensor camera images including a front and rear-facing camera image acquired via a same respective dual-sensor camera. The device assembles the rear-facing camera images into a scene image and renders the scene image at a display screen. The device receives an indication of a region-of-interest within the scene image and responsively: identifies a set of rear-facing camera images acquired within a predetermined threshold distance of the region-of-interest as determined using the respective metadata; and renders, at the display screen, front-facing camera images, from the dual-sensor camera images, associated with the set of rear-facing camera images acquired within the predetermined threshold distance of the region-of-interest.