Shopping centers and retailers are constantly looking for ways to improve the shopping experience for their customers. With the introduction of people counting systems for shopper traffic, store owners can now monitor the number of people entering and exiting the store, giving them insight into how busy the store is at any given time. This system not only helps store owners make better staffing decisions, but it also helps them optimize store layouts and promotions to better serve their customers. In addition, customers can benefit from this technology as well, with shorter wait times and a more organized shopping environment. As technology continues to advance, people counting systems are proving to be a valuable investment for businesses looking to enhance their customer experience.
About Our Client
Allow us to introduce a prominent client company rooted in the E-commerce industry, based in the country of Japan. This reputable organization has persistently strived to revolutionize the digital world by catering to the needs of the modern-day consumer.
By leveraging state-of-the-art technology and innovative business strategies, the organization has carved a unique identity for itself within the highly competitive marketplace. In addition to providing exceptional products, this company has also consistently set high standards for customer service, ensuring that consumers receive the attention and support they deserve. With a passion for success and growth, this client company is a shining example of excellence within the E-commerce industry in Japan.
Business Need
With the rise of technology, businesses are utilizing the latest tools to gain a competitive advantage. One such tool is the People Counting System, which has become an essential addition to the retail industry. By providing insight into shopper traffic, the system supplements surveillance cameras and helps retailers optimize their operations. With accurate data on footfall, businesses are better able to identify patterns, make informed decisions, and ultimately improve their retail business. This tool has proven to be a valuable investment for retailers seeking to maximize their efficiency and profits.
Accordingly, we are required to design and develop a People Counting System for our client’s retail stores. Our task is to build an effective system, which would not only provide data on the number of shoppers entering and exiting the store, but also help retailers optimize their layouts, promotions and staffing decisions. With this purpose in mind, we plan to utilize advanced technology Deep Learning in order to create an effective solution that caters to the needs of our valued client.
Our Approach
TensorFlow and OpenCV are the two valuable tools that we planned to utilize for this project. TensorFlow and OpenCV are powerful technologies that have revolutionized the world of machine learning and computer vision. TensorFlow, developed by Google, provides a flexible framework for building and training machine learning models. With its impressive abilities to handle large data sets and complex models, TensorFlow has become a go-to tool for many AI applications.
On the other hand, OpenCV is a popular and widely used computer vision library that enables developers to perform a variety of tasks such as object detection, face recognition, and image processing with ease. By combining these two tools, developers can create robust and efficient AI applications for a wide range of industries. Whether you’re working in healthcare, finance, or retail, the power and versatility of TensorFlow and OpenCV can help you build innovative solutions that make a real impact on your business or industry.
The Solution
Generally, in order to accurately detect and track individuals in a video, advanced technology utilizing deep learning was employed. The engine created for this task was built utilizing TensorFlow’s object detection API / Faster R-CNN. TensorFlow’s object detection API is a useful approach that allows developers to train models to identify objects in images and videos. One of the most popular algorithms used in this API is Faster R-CNN, which stands for “Region-based Convolutional Neural Network”. This algorithm is known for its accuracy and speed in detecting objects and has been used in a wide range of applications.
Once individuals were recognized, unique IDs were assigned through which they could be tracked in real-time. To achieve this, a tracking algorithm known as SORT / deep SORT was utilized. SORT stands for Simple Online and Realtime Tracking, which is a tracking algorithm that is very efficient and easy to use. Meanwhile, Deep SORT is an upgraded version of SORT and is much more sophisticated and complex. It uses deep learning to accurately identify and track objects over a longer period of time. With this technology in place, the task of detecting and tracking people in each video frame became not only achievable but streamlined.
Final Thoughts
In conclusion, we have developed an effective People Counting System that utilizes the power of Deep Learning to accurately detect and track individuals in a video. By combining TensorFlow’s object detection API with OpenCV and SORT / deep SORT algorithms, our system will be able to provide accurate data on footfall for retailers seeking to maximize their efficiency and profits. We believe this technology has great potential for other industries as well and look forward to seeing more innovative solutions being created utilizing these powerful tools.
If you have any business cases that require the use of advanced technology like our People Counting System, please get in touch and we’ll be happy to talk more!
