Object Detection Using Dynamic Thresholds for Temporal False Positives
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Solution Overview
Problem
Existing object detection models are susceptible to false positive detections due to the use of a single confidence threshold that cannot account for temporal changes in image sequences, leading to inaccurate identification of static objects as moving objects of interest.
Innovation Solution
Implementing a method that dynamically adjusts confidence thresholds based on temporal information derived from image sequences, distinguishing between static and moving objects by using different threshold levels for bounding boxes associated with motion or lack thereof.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single confidence threshold is used for object detection, then the detection process is simple and fast, but false positive detections increase due to inability to account for temporal changes
Solution Approach 1:
The patent applies dynamics by transitioning from a static single threshold to a dynamic multi-threshold system. The confidence threshold is no longer fixed but adapts based on temporal information from image sequences. The system selects from multiple predetermined thresholds (e.g., 0.5, 0.6, 0.7, 0.8, 0.9) based on the detected motion state, making the threshold dynamic and context-dependent rather than static and universal.
Solution Approach 2:
The patent changes the parameter of confidence threshold from a single fixed value to multiple variable values. By introducing temporal information as an additional parameter that influences threshold selection, the system can adjust the confidence requirement based on whether objects are moving or static. This parameter change enables the system to maintain higher reliability by using appropriate thresholds for different temporal contexts.
2Measurement precision
If a high confidence threshold is used to reduce false positives, then detection precision improves, but detection of moving objects may be missed due to temporal changes
Solution Approach 1:
The system dynamically adjusts the confidence threshold based on the temporal characteristics of detected objects. For moving objects, the system selects lower thresholds from the predetermined set to accommodate temporal changes and prevent missed detections. For static objects, higher thresholds are applied to maintain precision and reduce false positives. This dynamic adaptation resolves the contradiction between precision and temporal versatility.
Solution Approach 2:
The patent applies local quality by assigning different confidence thresholds to different objects based on their individual temporal characteristics. Rather than using a uniform threshold for all detections, the system evaluates each bounding box's temporal information independently and applies the appropriate threshold level. This localized approach allows high precision for static objects while maintaining adaptability for moving objects.
3Reliability
If temporal information processing is added to distinguish static and moving objects, then false positive reduction improves, but computational complexity increases
Solution Approach 1:
The patent segments the processing into distinct stages: first detecting objects and generating bounding boxes, then separately analyzing temporal information for each bounding box, and finally selecting appropriate thresholds based on temporal characteristics. This segmentation of the detection pipeline allows temporal analysis to be added as a modular component rather than complicating the entire system, improving false positive reduction while managing processing complexity through structured organization.
Solution Approach 2:
The system applies partial action by selectively processing temporal information only for bounding boxes that require further evaluation. Rather than performing exhaustive temporal analysis on all possible detections, the system focuses computational resources on bounding boxes where temporal information can meaningfully differentiate between true positives and false positives, thereby improving reliability without excessive computational overhead.
Data Source
AI summary
This disclosure provides methods, devices, and systems for object detection in images. The present implementations more specifically relate to object detection with dynamic confidence thresholds. In some implementations, an image analysis system may map a bounding box to a first image in a sequence of images based on an object detection operation that assigns a confidence score to the bounding box indicating a likelihood that an object of interest is included in the bounding box; determine temporal information associated with the first image based on a second image in the sequence of images; select one of a plurality of confidence thresholds based at least in part on the temporal information; and selectively discard the bounding box based on whether the confidence score exceeds the selected one of the plurality of confidence thresholds.