Indoor Gunshot Detection via Video Analytics and Sensor Fusion
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Solution Overview
Problem
Gunshot detection in indoor environments is challenging due to factors like extensive sound reverberations, convoluted acoustic pathways, and interference from noise and lights from fire alarms or loud music, making it difficult for law enforcement to quickly identify and track shooters.
Innovation Solution
Deploying one or more position-independent gunshot sensors that collect infrared and acoustic information, combined with video analytics to determine gunshot occurrences and track suspected shooters using image classifiers, allowing for real-time identification and tracking of individuals with video cameras and microphones activated only after a gunshot is detected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of moving object
If multiple microphones are spread out over large distances to detect gunshots, then gunshot detection range is improved, but false detection from similar sounds (car backfires, construction noises, fireworks) increases
Solution Approach 1:
The patent combines multiple sensing modalities (acoustic sensors, infrared sensors, video cameras, audio microphones) into an integrated gunshot detection system. This multi-sensory approach allows the system to cross-validate signals and distinguish true gunshots from false sources like car backfires or fireworks, thereby maintaining large detection range while reducing false detection rates.
Solution Approach 2:
The detection system is designed to perform multiple functions simultaneously: acoustic gunshot detection, infrared thermal detection, video recording, and audio capture. This multi-functional system can identify gunshot occurrences through multiple independent channels, improving reliability across diverse detection scenarios without requiring separate systems for each function.
2Speed
If video analytics are continuously performed to track suspected shooters, then shooter identification speed is improved, but computational resource consumption increases
Solution Approach 1:
The system performs preliminary gunshot detection using acoustic and infrared sensors before activating full video analytics. When a gunshot is detected, the system then engages video collection and analytics specifically for tracking the suspected shooter. This staged approach enables rapid shooter identification when needed while avoiding continuous computational resource consumption during normal operations.
Solution Approach 2:
Video analytics are activated periodically or event-driven rather than continuously. The system monitors for gunshot occurrences and only then initiates intensive video analysis and tracking functions. This periodic activation pattern maintains fast response capability for shooter identification while significantly reducing overall computational resource consumption compared to continuous analysis.
3Measurement precision
If audio microphones are activated continuously to track suspected shooters, then tracking accuracy is improved, but privacy concerns and energy consumption increase
Solution Approach 1:
Audio microphones are activated only after a gunshot occurrence is detected by the acoustic or infrared sensors. This preliminary detection triggers subsequent audio recording and analysis only when necessary for tracking a suspected shooter. This approach maintains high tracking accuracy when needed while minimizing unnecessary audio collection that would raise privacy concerns.
Solution Approach 2:
The audio microphone activation is periodic and event-driven rather than continuous. Audio recording is engaged only during and after gunshot events when shooter tracking is required, and deactivated during normal operations. This periodic activation preserves tracking accuracy during critical events while reducing privacy intrusions and energy consumption during non-critical periods.
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
Enables rapid and accurate detection and tracking of gunshots in indoor environments, reducing false alerts and improving public safety by providing real-time data to law enforcement, even in noisy or crowded conditions.
Implementation Method 1
collecting acoustic information within an indoor environment; analyzing the infrared information and the acoustic information to determine a gunshot occurrence
Implementation Method 2
collecting infrared information within an indoor environment using a gunshot sensor; analyzing the infrared information and the acoustic information to determine a gunshot occurrence
Data Source
AI summary
Gunshot detection within an indoor environment is performed with video analytics. One or more position-independent gunshot sensors are deployed. Infrared and acoustic information is collected within an indoor environment for indoor gunshot detection with video analytics. The infrared information and the acoustic information are analyzed to determine a gunshot occurrence. Video analytics for tracking a suspected shooter of the gunshot is performed, based on the gunshot occurrence, using video that is collected. A person of interest is tagged, as a portion of the video analytics, for the purpose of tracking the person of interest. Video collection from one or more video streams is engaged, based on the detection of the gunshot. The video analytics uses image classifiers. The image classifiers are used to identify a gun type. The suspected shooter is identified based on the video analytics. An audio microphone is activated based on the detecting of the gunshot.