Video Camera Event Detection Using Implicit Ground Truth Feedback
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Monitoring systems often suffer from false positives and delayed event reporting, negatively impacting user experience due to incorrect detection of events by doorbell cameras.
Innovation Solution
Utilizing implicit ground truth data from user interactions, such as pressing a doorbell, to adjust and refine the parameters of object detection models, thereby improving the accuracy and latency of event detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If object detection models are used to detect events in monitoring systems, then event detection capability is improved, but false positives and delayed reporting occur reducing accuracy
Solution Approach 1:
The system uses implicit ground truth data from user interactions (doorbell presses, package deliveries) as feedback to continuously adjust and refine object detection model parameters. This feedback loop improves detection accuracy by comparing model predictions against actual user behavior patterns, thereby reducing false positives while maintaining reliable event detection capability.
2Measurement precision
If model parameters are continuously adjusted to improve detection accuracy, then measurement precision is improved, but system complexity increases
Solution Approach 1:
The monitoring system performs self-adjustment of detection model parameters using implicit ground truth data automatically collected from user interactions. The system autonomously refines its detection algorithms without requiring manual intervention or complex external tuning processes, thereby improving detection accuracy while maintaining manageable system complexity through automated self-optimization.
3Measurement precision
If ground truth data is collected and processed to adjust model parameters, then detection accuracy is improved, but power consumption increases
Solution Approach 1:
The system utilizes inexpensive, easily collectable implicit ground truth data from routine user interactions (doorbell presses, package deliveries) rather than requiring expensive or energy-intensive active sensing. This approach improves detection accuracy by leveraging naturally occurring data events that consume minimal processing power and energy, treating each user interaction as a low-cost training opportunity.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for object detection. One of the methods includes determining, using first sensor data, a detection result on whether to trigger an event alerting a presence of an object in a target area by executing one or more models; determining, using second sensor data, a ground truth for the event that indicates whether an object is present in the target area; determining a difference value by comparing the detection result and the ground truth; adjusting at least one parameter of the one or more models in response to determining that the difference value does not satisfy the one or more threshold criteria; and determining a new detection result on whether to trigger a second event by executing the one or more models with adjusted parameters using new first sensor data.