SiPANON
Traffic Object Monitoring and Analysis System
A smart solution as an answer to the challenges of traditional surveillance systems. The Actionable Intelligence Surveillance System enables surveillance systems, which traditionally function as "monitors," to advance further with the integration of "follow-up" actions.
Background
General Condition of Traffic Surveillance Systems
Compliance with Indonesian Law No. 22 of 2009 on
"Traffic and Road Transport"
What you will learn?
- How cloud infrastructure helps reduce energy consumption and carbon footprint through efficient resource utilization and scaling
- Insights into best practices for securing cloud-based applications and data
- Tips for optimizing cloud costs, such as monitoring resource usage and rightsizing instances
- Examples of organizations leveraging the cloud to implement sustainable practices, improve security, and reduce costs
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All in Solutions
- Accelerated follow-up actions for improved service delivery;
- Assists stakeholders in making effective, efficient, and accurate decisions;
- Compliance with regulatory requirements, all of which can be met with SiPANON.
Features
CCTV Monitoring
Equipped with monitoring functions to ensure the CCTV is operating properly.
CCTV Data Collection
Conducting online identification and investigation of traffic issues swiftly.
CCTV Data Analysis
Using Edge Processing technology and AI Machine Learning to expedite precise and accurate analysis of traffic situations.
Integrated Follow-Up Actions
Integrated with follow-up features to facilitate local action and forward notifications when incidents are detected.
CCTV Data Storage
Stored in the cloud, making it easy to archive and more cost-effective.
How Does SiPANON Work?
Step 1
CCTV as input, capturing incidents of counterflow and illegal stopping.
Step 2
Incident Captures processed with Edge Technology on ELIBOX
Step 3
Notifications and Alerts on the dashboard
Step 4
Action Button for follow-up steps
Latest Insight
Learn More About SiPANON and Video Monitoring System