Page 44 - Security Today, July/August 2024

outdoor cameras, typically certified against water and dust ingress (IP66), may exhibit reduced sensitivity due to their sealed design.
In such cases, employing an external, strategically positioned microphone can greatly enhance the accuracy of audio analytics running outdoors. High-quality directional microphones, capable of mitigating wind noise, are recommended for critical audio data collection outdoors.
Any high-quality external microphone should easily outperform an internal microphone regarding analytic accuracy, so it is worth considering in areas where audio information gathering is crucial. AI sound classification is in the range of 200Hz to 8Khz, and the frequency distribution of a captured sound is an important charac- teristic during analysis. Therefore, a microphone must be able to pick up frequencies across this range with a flat or neutral characteristic.
AI SOCS ENHANCE ACCURACY
Recent advancements have seen the introduction of surveillance cameras equipped with dedicated AI System on Chips (SoC), such as the Ambarella CV52. This chip can perform both video and audio analytics simultaneously.
Using an SoC allows for integrating advanced features, including a sound database against which audio from the scene is compared for real-time classification. Deep learning algorithms make these comparisons even more accurate. For example, when identifying a sound, an i-PRO camera compares the captured sound volume level with a preset threshold value. If it is greater than the threshold, AI is then used to determine what kind of sound it could be.
With the goal of creating an AI-derived similarity score, the system determines whether the captured sound corresponds to any of the four target sound categories: yell, glass break, vehicle horn, and gunshot.
This is done by dividing the sound into regular segments, per- forming signal processing, and extracting relevant features that can be used for analysis and comparison. An AI inference calcu- lation uses machine learning algorithms to analyze the audio data and classify the audio data into distinct categories with a score based on similarity to the target sound. An alarm/notification is triggered when the similarity score exceeds a certain value.
CAMERA CONFIGURATION FOR AUDIO ANALYTICS
Audio detection. Proper configuration begins with setting a cam- era to detect relevant sounds while ignoring irrelevant back- ground noise. Since audio levels are typically louder in abnormal situations, cameras should be tailored to their specific environ- ments, and sound level thresholds should be set only to flag audio levels suggestive of unusual activity.
AI-based audio analytics should be trained to identify target sounds under various conditions, such as situations with typical environmental noise or other non-target sounds and at different distances. This reduces the possibility of false positives caused by background noise.
Source classification. Ensuring a high signal-to-noise ratio is crucial for accurate sound classification. Installers need to con- sider the placement of cameras and microphones to avoid areas
that may amplify background noise, which could skew the ana- lytics. For example, while a corner might be an ideal location for video coverage, it can be a poor choice for audio due to an artifi- cial amplification of background noise.
Making sense of alerts. Selecting a VMS that fully integrates with the camera’s API (application programming interface) is es- sential for capturing detailed audio analytic events. While stan- dards like ONVIF also support audio analytics messages, ad- vanced integration with VMS platforms can discern, categorize and search for audio-triggered events based on classification ID (i.e., glass break, car horn, gunshot, yell). It is important to ensure camera and VMS messaging handling methods are compatible.
Well-configured audio analytics can deliver an extra layer of situational awareness. They help validate what operators see on screen, allowing them to accelerate response times while provid- ing detailed insights that go beyond traditional video surveillance.
When a separate purpose-built audio system is beyond the budget, modern AI-enabled cameras can step up and reduce the overall cost of installing purpose-built glass break sensors at ev- ery point of ingress.
By effectively addressing privacy concerns, audio analytics allow for the responsible utilization of audio capabilities in security cam- eras. i-PRO AI-enabled cameras, for example, feature customizable settings for audio classification type, sensitivity, and detection levels, ensuring superior performance across multiple
installation environments. Pairing AI-enabled
cameras with audio analytics with a compatible
VMS is important to ensure success.
Rui Barbosa is the category manager for Surveillance Products at i-PRO Americas.
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JULY/AUGUST 2024 | SECURITY TODAY
AUDIO ANALYTICS