Clustering Visual Detections for Teach-by-Example Annotation
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Solution Overview
Problem
Automated security systems face challenges in efficiently prioritizing and annotating visual object detections from video data, leading to high false positive rates and time-consuming annotation processes for security personnel.
Innovation Solution
A method and system that cluster and prioritize detections based on perceptible categories, allowing users to digitally annotate true or false positives, and adjust criteria for reassigning non-annotated detections, utilizing a combination of local and cloud-based processing for improved accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated security systems process all visual object detections without prioritization, then comprehensive monitoring is achieved, but annotation time and processing burden increase significantly
Solution Approach 1:
The patent segments detections into different priority levels (first priority, second priority, etc.) based on clustering of perceptible categories. This segmentation allows security personnel to focus annotation efforts on high-priority detections first, thereby increasing overall annotation throughput while managing time effectively.
Solution Approach 2:
The system performs preliminary clustering and prioritization of detections before the annotation process begins. By pre-organizing detections into priority-based clusters, the system prepares the data in advance, allowing annotators to work more efficiently without spending time on initial sorting or triage.
2Reliability
If traditional object detection methods are used without clustering, then all detections are processed uniformly, but false positive rates remain high and review efficiency decreases
Solution Approach 1:
The patent segments detections into multiple perceptible categories (e.g., human, vehicle, animal) and further divides them into priority clusters. This segmentation enables the system to identify and flag suspicious categories separately from normal detections, improving detection accuracy while allowing reviewers to efficiently process high-priority items first.
Solution Approach 2:
Different priority levels are assigned to different clusters based on their suspiciousness or importance. High-priority clusters receive more attention and resources for review, while low-priority clusters are processed with less intensity. This local quality differentiation improves both reliability for critical detections and productivity for the overall system.
3Measurement precision
If security personnel manually review all detections individually, then thorough analysis is achieved, but the process becomes extremely time-consuming and labor-intensive
Solution Approach 1:
The patent segments the large volume of detections into manageable priority-based clusters. Security personnel can systematically work through high-priority clusters first, ensuring thorough analysis where it matters most, while maintaining reasonable review speeds through the structured approach.
Solution Approach 2:
The system provides feedback to security personnel by highlighting the priority level and category of each detection cluster. This feedback mechanism guides annotators on where to focus their attention, improving both the precision of annotations and the efficiency of the review process by reducing cognitive load and decision-making time.
Data Source
AI summary
Clustering and active learning for teach-by-example, and methods therefor, are disclosed. One method includes clustering, at an at least one electronic processor: a plurality of first detections together as a first cluster based on each detection of the first detections corresponding to respective first image data being identified as potentially showing a first perceptible category of a plurality of perceptible categories; and a plurality of second detections together as a second cluster based on each detection of the second detections corresponding to respective second image data being identified as potentially showing a second perceptible category of the perceptible categories.