Know exactly how many vehicles are at your locations, right now.
Safari AI turns your existing cameras into a live vehicle analytics system. Track traffic patterns, parking utilization, and delivery activity by zone to optimize operations and reduce bottlenecks.
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Without vehicle data, parking, logistics, and forecasting are all guesswork.
Most facilities have no reliable way to measure vehicle volume by zone, track dwell time, or detect unauthorized activity in real time. The result is wasted space, enforcement blind spots, and staffing decisions based on gut feel.
- —No real-time visibility into which lots or lanes are full, underused, or blocked
- —Illegal or unauthorized vehicle activity goes undetected until a complaint or incident
- —Demand forecasting relies on historical estimates rather than precise daily counts
- —Staff allocation at loading docks and drive-thrus is reactive, not data-driven
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 data 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 Malls
#2 Outlet Operator in North America
The second-largest outlet operator in North America optimizes guest experiences by measuring parking utilization, vehicle flow tracking, people counts, guest dwell times, and restroom usage analytics.
Read Outlet Operator Case Study Retail & Shopping MallsEverything vehicle analytics should do, and actually does.
Monitor vehicle flow to improve parking management and detect capacity issues.
Identify legal and illegal vehicle activity in designated zones in real time.
Precise volume data to forecast demand and allocate staff based on predicted activity.
Measure how long vehicles occupy each zone to optimize turnover and flow.
Traffic Pattern Analysis
Monitors vehicle traffic patterns across parking lots, loading docks, and drive-thrus to help facilities improve space management and detect capacity issues before they back up.
Vehicle Activity Detection
Accurate count data enables facilities to detect legal and illegal vehicle activity in real time, ensuring proper enforcement of designated zones without relying on manual patrols.
Demand Forecasting
Precise vehicle volume measurements enable organizations to forecast operational demand and allocate staff resources based on predicted activity, reducing both under and overstaffing.
BI & Operations Integration
Push live vehicle count data into Tableau, Power BI, Snowflake, or your operations platform via REST API. Safari AI fits into your existing analytics stack with no bespoke middleware required.
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).