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See how WildFaces' NO Deep Learning AI can contribute to keep Hong Kong Clean

We would like to invite you to visit our booth 3E-A03 at InnoEX 2024 to experience our NO deep Learning AI system.


Hong Kong has introduced legislation requiring organizations to ensure that garbage is deposited in appropriately colored garbage bags. WildFaces has helped organizations ensure that this requirement is met.

Cleanliness, Good Housekeeping and Effective Waste Management have become a high priority for Cities and large organizations.

Refuse Rooms have to be properly managed and must not start smelling. Waste bins must not overflow. Waste collection fleets must operate effectively and waste disposal plants must be safe and not exude toxic smells towards their neighbors.

WildFaces’ Multi-Sensory AI Analytics capability (video, sound and smell) can help organizations ensure that:

• Bad smells emanating from the Refuse Room are detected (see e-Nose)
• Overflowing garbage bins and bin covers that have not been shut properly are detected to reduce smells and vermin (see video) 
• Refuse rooms are kept clean and are able to send alerts when the garbage is littered outside the bins (see video)
• Designated garbage bags are used (see video)
• The presence of rats and other vermin in the Refuse Room is detected (see video)

And alarms can be sent to the nearest “cleanliness” team to ensure that issues can be immediately addressed.

WildFaces at InnoEX (Booth #: 3E-A03)

Click HERE to register

About WildFaces:

WildFaces’ patented “On-The-Move” Artificial Intelligence (AI) based analytics system, WildAI, provides video, sound and smell analytics from moving sensors/ cameras on drones, moving robots and body-worn cameras. Such systems have been implemented on numerous government and commercial sites worldwide.

Applications range from anonymized tracking (with privacy protection) and traffic congestion management to sound and smell analytics.

WildAI requires minimal training, is computing light (does not require GPUs) and can be deployed very quickly. It:

• Operates in real-time even when the sensor is “On-the-move”.
• Requires little data training – no labelling, no deep learning
• Is infrastructure light – fewer cameras required
• Is computation light

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