Video Camera Object Detection With Implicit Event Ground Truth
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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 parameters of object detection models and improve accuracy and latency of event detection, without requiring hardware replacement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If object detection models use traditional sensor data only, then hardware complexity remains low, but detection accuracy and reliability deteriorate due to false positives and delayed event reporting
Solution Approach 1:
The patent combines data from multiple sensor types (audio sensors detecting doorbell presses, video cameras capturing visual data, motion detectors tracking movement) to create a multi-modal detection system. This merging of sensor data sources enables cross-validation of events, reducing false positives and improving both accuracy and reliability of event detection without requiring hardware replacement
Solution Approach 2:
The system implements feedback mechanisms where detection results are continuously evaluated against ground truth data derived from implicit user interactions. The model parameters are automatically adjusted based on this feedback loop, improving detection accuracy over time while maintaining system reliability through continuous optimization
2Measurement precision
If model parameters are adjusted frequently to improve detection accuracy, then measurement precision improves, but loss of time increases due to repeated parameter tuning and model retraining
Solution Approach 1:
The system performs preliminary actions by collecting and storing ground truth data from implicit user interactions (doorbell presses, package deliveries) during normal operation. This pre-collected data is then used for batch parameter adjustments rather than frequent real-time tuning, reducing the time loss associated with continuous model retraining while maintaining high detection accuracy
Solution Approach 2:
Parameter adjustments are performed periodically based on accumulated ground truth data rather than continuously. The system evaluates detection performance at scheduled intervals and adjusts parameters only when significant improvements are warranted, balancing accuracy enhancement with time efficiency
3Reliability
If multiple sensor types are integrated to improve detection reliability, then event detection reliability improves, but device complexity increases due to additional sensors and data processing requirements
Solution Approach 1:
The system achieves multi-functionality by using a centralized processing unit that handles data from multiple sensor types (audio, video, motion). This universal processor performs multiple functions including event detection, ground truth validation, parameter optimization, and cross-sensor correlation, improving reliability without proportionally increasing device complexity through specialized components for each function
Solution Approach 2:
The patent introduces an intermediary processing layer that mediates between multiple sensor inputs and the detection model. This intermediary component standardizes data formats, synchronizes temporal information across sensors, and performs initial filtering before data reaches the main detection algorithm, reducing the complexity burden of multi-sensor integration
4Measurement precision
If ground truth data is collected from explicit user feedback, then measurement precision improves, but ease of operation deteriorates due to requiring user participation and interaction
Solution Approach 1:
The system performs self-service by automatically collecting ground truth data from implicit user interactions without requiring explicit user feedback. Doorbell presses, package deliveries, and other events are detected and recorded as ground truth through the system's own sensor network and data processing capabilities, maintaining high measurement precision while eliminating the need for user participation in data collection
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.