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Taipei Tzu Chi Hospital, established by the Tzu Chi Foundation, is a major medical institution located in Xindian District, Taipei City, Taiwan. Serving as a healthcare hub for the area, it boasts comprehensive facilities such as a library and conference center. The hospital experiences heavy daily traffic of vehicles and people.
Consequently, it encounters a significant challenge in parking resource management, especially during peak hours when patients and visitors frequently encounter difficulties finding parking spots. In response, the hospital has introduced an advanced license plate recognition system to streamline parking management efficiency and enhance overall user satisfaction.
The traditional way of managing parking, where tickets are issued and used for parking information and billing, is outdated today. Just managing, finding, and possibly losing these tickets can incur significant risks and time costs. Imagine if every driver had to spend time inserting a card at the gate—it would significantly extend the queue times for entering and exiting traffic flow.
The parking lot frequently becomes congested, making it difficult to find available spaces. This not only impacts the medical experience of patients and visitors but also adds to the hospital's administrative challenges.
The hospital has decided to implement an advanced license plate recognition system to enhance parking management through automation. Key features of the system include automatic license plate recognition, real-time parking space availability information, and intelligent billing.
Cameras installed at the entrance and exit of the parking lot automatically identify vehicle license plates, eliminating the need for manual intervention and reducing labor costs.
The system records the entry and exit times of each vehicle in real-time and calculates parking fees automatically based on the duration of parking. Drivers can conveniently make payments through various methods such as mobile payments or credit cards, minimizing wait times during exit.
LILIN has been a longstanding provider of license plate recognition solutions, and in recent years, we advanced these systems with artificial intelligence, notably enhancing the accuracy of plate recognition. Beyond identifying license plates, the system also provides clear vehicle type information.
Importantly, all recognition tasks are handled directly through front-end cameras, eliminating the need for additional computing equipment and significantly streamlining the overall architecture.
The license plate recognition tasks are handled by LILIN Z7 series AI cameras. These cameras include built-in access control lists, which make managing vehicle entry and exit more convenient. For example, they can differentiate between employee or management vehicles and visitors, ensuring precise implementation of various access control levels.
Following the implementation of the license plate recognition system, traffic management in the parking lot has become more efficient, effectively reducing congestion. The automated system has minimized the requirement for manual oversight, thereby lowering operational costs, while also enhancing data accuracy and management transparency.
Moreover, the Z7 cameras facilitate data integration through Python or C# programming languages, allowing the management to interface with the cameras via open APIs and expand their functionality.
"There's no need for owners to alter their existing IT framework," noted Steve Hu, CIO of LILIN. "From a hardware standpoint, it simply involves replacing old cameras with LILIN AI cameras to finalize the deployment. Just that easy."
LILIN AI cameras are widely trusted in industry, government, and academic sectors, with proven applications in various fields.
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