People counting or crowd counting is used across various industries with floating customer traffic such as – Banks, Hospitality, Healthcare, Retail Spaces, Smart Cities, Schools, Museums – Places where people queue to get services or they enter or pass through a specific region and require tracking as an important tool for several purposes such as
While the usage of IP-based surveillance video cameras has already increased many folds, they can also be augmented for people counting systems to not only count people but even detect user behavior using computer vision and ML models.
Thus, existing IP based digital cameras can be used to extract real-time video analytics and can prove to be effective customer feedback systems.
Currently, People counting systems can monitor the queues or entry points at airports, retail outlets, customer counters to benefit organizations with continuous surveillance insights. But with the growth of newer technologies like Computer Vision and AI, these systems can be easily extended to provide analytics, queue management, space utilization, Heat Map, dwell time, and even Visitor tracking across stores.
The solution enables you to easily track the number of customers entering and leaving an area through a “single entrance area”.
Using CV and AI-based software that works on any IP camera is beneficial compared to other technologies with cost-effectiveness, flexibility, and a number of uses case capabilities.
For hotels that are very sensitive towards the needs of their customer base, they can get valuable insights and drive personalized customer experience by
- Statistical Purpose – Visitors Footfall, Visitor Demographic profile
- Marketing Research – Measuring Customer Experience, Customer Wait Time, Time spent in Layout Areas
- Operational insight for decisions – Arrival pattern, People count v/s Capacity management
- Security – Increased surveillance, Fast response, and Control
Types of People Counting Systems
A whole range of People Counting solutions with differential features are available in the market – Some are most elementary and LED/sensor-based and can only detect the ‘Person Count’. 2D and 3D people counting systems have been widely adopted in different end-user verticals, including retail, transportation, hospitality and healthcare. 2D people counting systems use a camera and single-lens to count how many people have passed through an environment. They are most useful for low-traffic or well-lit areas. 3D & 4D people counters use stereoscopic vision to track visitors’ movements and accurately count how many people are in the store. They use Wi-Fi or Bluetooth for visitor tracking. However, store metrics have limited the effectiveness in certain areas, such as spots that are affected by shadows or changing light levels. Video-based counters may not be accurate if they are being used in these areas. Additionally, as they are powered with electricity and require continuous Wi-Fi for data transfer, to overcome this challenge, retail store owners have to install multiple video-based counters integrated with the latest technologies, in various places inside the store, which incurs additional cost.
While the usage of IP-based surveillance video cameras has already increased many folds, they can also be augmented for people counting systems to not only count people but even detect user behavior using computer vision and ML models.
Thus, existing IP based digital cameras can be used to extract real-time video analytics and can prove to be effective customer feedback systems.
Currently, People counting systems can monitor the queues or entry points at airports, retail outlets, customer counters to benefit organizations with continuous surveillance insights. But with the growth of newer technologies like Computer Vision and AI, these systems can be easily extended to provide analytics, queue management, space utilization, Heat Map, dwell time, and even Visitor tracking across stores.
How Do You Count People with Computer Vision and Deep learning?
Just like the human mind utilizes its neural networks to connect and process, series of images seen, AI and computer vision has brought new “intelligence to the eyes” of Cameras. It can count People passing by and estimate the population density of a crowd, the statistics of people counted provide valuable information such as customer interests, customer wait times, path analysis, and more that are critical to customer experience management. Using CV and deep learning People counting is based on object detection, detecting people using neural networks. To create an object counter we use object detection in combination with a region of interest focus on a specific image region. Such CV capabilities can be packaged as flexible, micro services-on demand, for different “regions of inspections”. They can be deployed in conjunction with an existing, network of cameras that may be connected to the output display at the premises, giving real-time insights.
The solution enables you to easily track the number of customers entering and leaving an area through a “single entrance area”.
Using CV and AI-based software that works on any IP camera is beneficial compared to other technologies with cost-effectiveness, flexibility, and a number of uses case capabilities.
- Monitor the count of people in various zones within a site and get predictable analytics for customer arrival pattern
- Controlling maximum entry numbers in a particular area and thereby adhere to social distancing guidelines
- Capture images/video for a customer’s demographics or interest profile, their shopping journey to get analytics
- Ability to understand busiest and quietest hours
- Set queue occupancy levels, no. of cash counters and measure customer billing/ service time
- Set and receive alerts and/or notifications via audio, visual, email or text when these capacity limits are approaching or reached.
Go beyond just people counting
1. Measure Customer Engagement and Experience
Businesses can make effective decisions, with continuous feedback and intelligence. For instance, Retailers need technology to maximize their ROI of the retail space by timely and continuous insights, accurate and reliable metrics for their customers. With the use of computer vision and deep learning they can –- Calculate a store’s conversion ratio (Arrivals: Buyers)
- Calculate each store’s footfall and heatmap patterns
- Compare store performance across stores for a particular product line and get insights
For hotels that are very sensitive towards the needs of their customer base, they can get valuable insights and drive personalized customer experience by
- Understand customer’s arrival/ and service usage patterns
- Improve layouts and customer service
- Optimize time-based staffing levels
- Staff behavior and engagement