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自动统计访客人数,实现 98% 的客流统计准确率

作者:Richard Ellor
7 December 2020
1 分钟阅读
自动统计访客人数,实现 98% 的客流统计准确率
Interactive CalculatorOccupancy & Sensor ROI

Visitor counting sensor accuracy and labor ROI advisor

Evaluate 98% accuracy 3D optical sensors versus manual tally clickers and WiFi presence analytics. Calculate annual door staffing cost savings and simulate automated entrance signalling.

Annual Door Staffing Cost

$102,200

Based on 7,300 manual door hours

Annual Automation Savings

$86,870

Net savings after 3D sensor infrastructure amortisation

Daily Counting Accuracy

98.2%

300 missed by WiFi vs 50 by 3D

Daily Uncounted Visitors

50 vs 700 manual

650 fewer missed counts per day

Live automated entrance signalling simulator

Simulate dynamic digital screen signalling (Stop-and-Go) connected to Purple occupancy APIs.

Occupancy Level: 80% (240 / 300)WARNING: CAPACITY NEAR LIMIT
🟢

WELCOME - PLEASE ENTER

Capacity available: 60 persons

🟡

APPROACHING LIMIT

Safe social distancing in progress

🔴

PLEASE WAIT

Entry paused until current visitors exit

Comparing 3D stereoscopic cameras, WiFi probes, and manual counting

3D Time-of-Flight (ToF) and stereoscopic optical sensors create height-depth maps that track moving heads with 98% to 99% accuracy. Unlike legacy infrared break-beam counters, 3D sensors accurately distinguish between adults, children, shopping trolleys, and groups walking abreast.

3D Optical & ToF Sensors (98%+)

Immune to shadows, sunlight glare, and darkness. Bidirectional in/out line crossing verification.

WiFi Presence Analytics (88%)

Captures dwell time, repeat visitor loyalty, and cross-zone journey paths without added camera hardware.

Automate your venue counting with 98% accuracy

Eliminate door staffing costs and unlock real-time footfall intelligence with Purple's hardware-agnostic platform.

Request a Platform Demo

人工统计进出人数是一种低效的游客数据收集方式。此外,许多场所不得不雇用额外的工作人员来协助完成这项工作。 

全球各地的场所,例如超市和繁华街区的零售商,都选择实施 “一进一出”策略, 即安排一名员工站在入口处,控制进出商店的访客流量。

自采用人工统计游客人数以来,企业的运营效率有所下降,这对其底线产生了负面影响。


这是因为人工统计人数时可能会发生一些人为错误 - 工作人员可能会分心、漏算,或者在短时间内离开岗位。

在确保场所内任意给定时间的占用人数不超过规定上限显得尤为重要的今天,企业在进行人数统计时,绝对无法承受低准确率带来的风险。 

准确计算人数以提高场所安全性

如上所述,人工统计人数可能会导致轻微的错误和漏算。在国际公共卫生危机期间,这种错误是任何企业都无法承受的。

企业可以通过多种方式利用技术来提高游客、员工和场所的安全性。 

通过将摄像机与我们的企业级分析平台连接,场馆可以使用兼容 3D 的摄像机追踪游客数量,准确率高达 98%。

通过精确统计进出场所的游客人数,企业还可以确保游客和员工之间能够保持安全社交距离,从而遵循政府的指导方针。 

由于收集的占用人数和数据都是匿名的,企业在让游客进入场所时也可以让他们更加安心。

自动控制进入场所的游客流量

使用 Purple 的自动化人数计算与分析 等软件的场所不仅能够计算进出场所的访客数量,还可以做得更多。 

企业可以使用信号系统,在场馆入口处的屏幕上或场馆内的特定区域进行显示,指示访客可以安全进入(绿色),还是必须等待(红色)直到场馆或区域内的访客人数降至安全水平。

这可以通过 API 直接从 Purple Hub 提取实时数据来实现,让您的游客高枕无忧。

利用这项技术自动控制进出场所的游客流量,意味着排队管理会变得极其高效。 

场所不再需要依赖工作人员手动统计游客人数并估算可以进入的人数。

除此以外,您还可以利用这些实时数据在网站上更新占用率和繁忙时段,或者通过自助终端和显示屏,实时展示场所内的游客人数。

正如那些已经适应自动化的企业所乐于证明的那样,运营效率和安全性都得到了提升,游客和客户的数量也因此不断增加。

这让那些仍在使用手动方式进行客流和人数计算的企业显得有些落后。

了解有关我们 客流控制和人数计算解决方案 的更多信息

常见问题

How do 3D stereoscopic sensors achieve 98% people counting accuracy?

Overhead 3D stereoscopic and Time-of-Flight (ToF) LiDAR sensors capture high-resolution depth maps from above doorways. By measuring the physical height, shoulder width, and vector trajectory of moving objects, the edge processors track individual human heads with 98% to 99% accuracy while filtering out shadows, floor reflections, shopping trolleys, and prams.

What is the difference between optical 3D sensors and WiFi presence analytics?

Optical 3D sensors provide bidirectional line-crossing counts at specific physical entryways to determine exact doorway volume. WiFi presence analytics measure smartphone probe requests across your wireless access points, providing broader venue metrics such as dwell time distribution, cross-zone flow, and return visitor frequency without needing line-of-sight overhead cameras.

How does automated entrance signalling control visitor capacity?

Purple connects live sensor data to digital screens or LED indicator lights at entrances via real-time webhooks. When current occupancy approaches safe operational limits, the system dynamically shifts the display from green (enter) to red (please wait), automating customer flow control without requiring dedicated security personnel at every doorway.

Can automated people counting distinguish between staff, customers, and shopping carts?

Yes. Advanced 3D sensors use height filtering (typically ignoring objects under 1.2 metres to distinguish luggage and trolleys from adults) and spatial mass analysis. Venues can also integrate BLE staff badges or WiFi authentication to automatically subtract active on-duty employees from public customer occupancy figures.

How does real-time occupancy data connect to websites and building management systems?

Purple provides REST APIs and WebSocket streams that push occupancy counts in sub-second intervals. Venues use these endpoints to feed live 'busy hour' widgets on consumer websites, automate HVAC and lighting adjustments in building management systems (BMS), and alert store managers when queue wait times exceed defined thresholds.

Is automated people counting compliant with GDPR and privacy regulations?

Yes. 3D optical and ToF sensors do not capture, record, or store identifiable facial images or personal data; all spatial processing occurs on edge hardware before discarding visual frames. WiFi location analytics use one-way salted cryptographic hashing on device MAC addresses, ensuring compliance with GDPR, CCPA, and global privacy standards.

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