Identify every vehicle at your locations.
Safari AI turns your existing cameras into a live vehicle classification system. Identify vehicle types, enforce parking policies, and optimize zone allocation without new hardware.
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Without classification data, parking enforcement and zone optimization are guesswork.
Most facilities cannot distinguish between vehicle types in real time. The result is unauthorized vehicles in restricted zones, wasted parking revenue, and enforcement that arrives too late.
- —No way to distinguish cars from trucks, delivery vehicles, or unauthorized types in real time
- —Parking policy violations go undetected until complaints surface, often too late to act
- —Zone allocation is based on assumptions rather than real data on which vehicle types actually show up
- —Revenue from loading zones and permit spaces is undermined by undetected non-compliance
Validated against ground-truth manual counts at deployment. If a camera view underperforms, we retune before you go live at no cost.
Leverage your existing cameras. No construction. Live in under two weeks.
Camera Review
We assess your existing CCTV or IP camera feeds remotely. Compatible views proceed; incompatible ones are flagged before any commitment.
On-Prem Deployment
A compact server is installed on-site and connected to your camera streams. All video is processed locally. Nothing leaves your network.
Calibrate & Go Live
Models are validated against manual counts. Once accuracy is approved, you're live with real-time dashboards and API access from day one.
How leading operators use Safari AI vehicle classification to drive decisions.
Anakeesta
Anakeesta leverages existing cameras to measure real-time KPIs including dispatch optimization, real-time occupancy monitoring, predicted wait times, and vehicle traffic analysis.
Read Anakeesta Case Study Theme Parks & Cultural Attractions
Brightline
Brightline assesses curbside activity surrounding train stations through vehicle counts and dwell time data to enhance management of their mobility fleet operations.
Read Brightline Case Study Parking, Garages & Loading Dock Management
Stanford
Stanford measures KPIs at loading zones including vehicle counts and dwell time analysis to optimize usage of limited space and inform planning for future construction.
Read Stanford Case Study Parking, Garages & Loading Dock Management
Goodwill of Kentucky
Goodwill of Kentucky enhances donation forecasting by measuring vehicle counts at drive-thru donation lanes across their 68 statewide stores.
Read Goodwill Case Study Retail & Shopping MallsEverything vehicle classification should do, and actually does.
Identify vehicle types, makes, models, and colors in real time.
Distinguish authorized from unauthorized vehicles without manual patrols.
Ensure each vehicle category uses its designated area correctly.
Maximize revenue from parking and loading assets with enforcement data.
Vehicle Type Identification
Identifies vehicle types, makes, models, and colors to help businesses allocate appropriate parking spaces and ensure authorized vehicles use designated areas, without any manual checking.
Automated Enforcement
Advanced classification distinguishes between authorized and unauthorized vehicles in real time, reducing illegal parking and improving space management without relying on manual patrols or complaints.
Zone Optimization
Real-time monitoring ensures different vehicle categories use their designated areas properly, giving facilities the data they need to optimize zone usage and reduce conflicts between vehicle types.
Curb Space Management
Precise classification data supports better enforcement decisions, enabling businesses to maximize revenue from parking and loading assets by identifying non-compliance before it compounds.
Frequently Asked Questions
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Safari AI delivers 99%+ accuracy on pedestrian and footfall counts across indoor and outdoor environments. Accuracy is validated against manual ground-truth counts during deployment, and our computer vision models are trained on enterprise-scale datasets from theme parks, stadiums, retail destinations, and QSRs. If a camera view underperforms, we tune the model to your specific environment before you go live.
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No. Safari AI works with the CCTV and IP cameras you already have — no camera rip-and-replace, no construction, no re-wiring. Deployment requires an on-premise server to process the video feeds locally at your site, which we spec and configure as part of onboarding. Your existing camera infrastructure stays exactly as it is.
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Most customers are live within days to a few weeks, depending on server provisioning and site access. After an initial camera review to confirm compatibility, we install the on-prem server, connect your existing camera feeds, calibrate the models, and validate accuracy against your baselines.
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Safari AI is built for high-density venues — we measure crowd counts and pedestrian flow at theme parks, NHL and NBA arenas, outlet centers, and stadium concourses. Our models handle occlusion, overlapping visitors, and non-linear movement patterns that break traditional sensor-based or beam-break counting systems. Reference clients include LEGOLAND, the Charlotte Hornets, and the Calgary Flames.
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Yes. Counts and analytics are available through live dashboards, scheduled exports, and REST APIs, which means you can pipe footfall data into Tableau, Power BI, Snowflake, your POS, or any internal system. Most enterprise customers run Safari AI alongside existing BI and RevOps workflows rather than as a standalone dashboard.
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Pricing is per-camera and scales based on the number of cameras, sites, and measurements you need — pedestrian counts, occupancy, dwell time, queue wait, and more can be layered on the same feeds. We offer a free 90-day pilot using your existing cameras with no credit card required, so you can validate accuracy and ROI before committing. Contact us for a tailored quote.
See exactly what your cameras can do.
Evaluate Safari AI on your existing camera infrastructure for 30 days. No credit card, no commitment.
30-day free pilot · No credit card required · Uses your existing cameras · Video processed on-premise
Frequently Asked Questions
How accurate is Safari AI's footfall counting?
Safari AI delivers 99%+ accuracy on pedestrian and footfall counts across indoor and outdoor environments. Accuracy is validated against manual ground-truth counts during deployment. If a camera view underperforms, we retune the model to your specific environment before you go live, at no additional cost.
Do I need to replace my cameras to use Safari AI?
No. Safari AI works with the CCTV and IP cameras you already have. No rip-and-replace, no construction, no re-wiring. An on-premise server is installed to process video locally; your existing camera infrastructure stays exactly as it is.
How long does Safari AI deployment take?
Most customers are live within days to a few weeks. After a camera compatibility review, we install the on-prem server, connect camera feeds, calibrate the models, and validate accuracy against your baselines before going live.
Can Safari AI handle high-density crowds?
Yes. Safari AI is built for high-density venues — theme parks, NBA and NHL arenas, outlet centers, and stadium concourses. Models handle occlusion, overlapping visitors, and non-linear movement that defeats traditional beam-break sensors. Clients include LEGOLAND, Charlotte Hornets, and Calgary Flames.
Can Safari AI integrate with Tableau, Power BI, Snowflake, or our POS?
Yes. Footfall counts and analytics are available via live dashboards, scheduled exports, and a REST API. You can pipe data into Tableau, Power BI, Snowflake, your POS, or any internal system. Most customers run Safari AI alongside existing BI and RevOps workflows.
How does Safari AI pricing work?
Pricing is per-camera and scales with the number of cameras, sites, and measurement types. Pedestrian counts, occupancy, dwell time, queue wait time, and more can be layered on the same feeds. A free 30-day pilot with no credit card required is available so you can validate accuracy and ROI before committing.
IR beam-break sensors are documented to miscount in high-traffic or wide-entrance conditions due to simultaneous crossings and non-human obstructions. Accuracy in crowd conditions can fall to 60 to 85%, representing a 15 to 40% undercount error. Sources: People Counting Systems — Infrared Sensors; V-Count — People Counting Technologies Guide; Milesight VS360 IR Sensor (up to 80% accuracy noted).