Indoor Camera Audio Event Classification for Automated Alarm Triggering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional security and automation systems in indoor environments lack the capability to trigger alarms based on audio detection, requiring manual intervention for event confirmation.
Innovation Solution
An indoor camera system that integrates audio and video detection to autonomously classify events like glass breaking, carbon monoxide alarms, or smoke alarms, triggering alarms or notifications without user interaction, and records relevant video footage for analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional security systems use motion sensors or glass break sensors to trigger alarms, then alarm triggering capability is provided, but these sensors require manual intervention and are not configured in many homes and businesses
Solution Approach 1:
The indoor camera is designed to perform multiple functions: standard surveillance, audio detection, and alarm triggering. By making the camera multi-functional, the system eliminates the need for separate specialized sensors while enabling automated alarm triggering based on audio event detection.
Solution Approach 2:
The camera system autonomously detects audio events, classifies them, and triggers alarms without requiring manual sensor configuration or intervention. The system serves itself by using its own audio detection capabilities to initiate security responses.
2Measurement precision
If indoor cameras are configured to trigger alarms based on audio detection, then surveillance precision and response effectiveness are improved, but the system complexity increases
Solution Approach 1:
An audio event classification model serves as an intermediary between the audio detection capability and the alarm triggering function. This model processes detected audio events, classifies them into relevant categories, and determines whether alarm triggering is appropriate, thereby managing system complexity through modular processing.
3Loss of time
If the camera autonomously classifies and responds to audio events, then response time is reduced, but false positives may increase
Solution Approach 1:
The system incorporates feedback mechanisms where detected audio events are analyzed and classified before triggering alarms. The classification model provides feedback on whether detected sounds warrant alarm activation, allowing the system to filter out false positives while maintaining rapid response times for genuine threats.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances surveillance precision by automatically responding to potential threats, reducing false positives, and ensuring timely alerts and responses.
Implementation Method 1
The indoor camera can detect, by analyzing audio data captured by the indoor camera, an audio event
Data Source
AI summary
Presented herein are system and methods for an indoor camera that can detect an audio event. The system can include an indoor camera, a control panel, and one or more processors coupled with non-transitory memory. The indoor camera can monitor an indoor environment. The indoor camera can capture audio data and visual data. The visual data can include images and/or videos. The indoor camera can detect, by analyzing audio data captured by the indoor camera, an audio event. The indoor camera can classify the audio event into a type of audio event. The indoor camera can transmit a message to the control panel, wherein the message indicates a type of the audio event. The control panel can receive messages and/or notifications from the indoor camera. The control panel can activate an alarm upon receiving the message having a particular type of audio event.