Case Studies

Platform

A large warehouse dealing with various goods faced challenges in tracking inventory levels due to the manual box counting process. The ware house staff had to spend hours counting boxes, which often resulted in inaccurate inventory reports. The manual object counting process also led to delays in restocking and fulfillment of customer orders. The warehouse management realized the need to adopt a more efficient and accurate method of box counting.

Background

Warehouses are a crucial part of any business that deals with physical goods. It is essential to maintain a proper inventory of goods to ensure efficient supply chain management. However, manual counting of boxes in a warehouse can be a time-consuming and tedious task, especially for large warehouses.

Solution

To solve the problem, the warehouse management team decided to implement a multi-object counting tracking analytics software. The software vendor installed cameras strategically in the warehouse entrance to capture video footage of box movements. It used advanced machine learning algorithms to analyze the video footage and count the number of boxes accurately. The software also generated real time reports of inventory levels and alerted the staff when the inventory levels fell below the required levels. The software also had features to detect anomalies such as misplacement, damage, and goods that could lead to safety hazards. The software alerted the staff in such cases so that they could take quick corrective action.

Result

The automated box counting process resulted in a more accurate and timely inventory report, which helped the warehouse management make better informed decisions about restocking and order fulfillment. The staff was relieved of the tedious task of manual counting, which improved their productivity and morale. The software also improved safety in the warehouse by detecting anomalies, leading to a safer working environment for the staff.

Conclusion

Service Category

Detection of wearable food safety equipment using Computer Vision solution with Yolov5s on existing CCTV camera setup for quality check.

Problem

One day, during a routine inspection, the restaurant manager found that one of the kitchen’s essential wearable food safety equipment, a chef’s hat, was not worn by a few members of the kitchen staff. Without the chef’s hat, there is a risk of head hair falling into the food.

Background

A US-based restaurant is a popular dining destination known for its delectable menu items and exceptional service. The restaurant’s kitchen staff is required to wear food safety equipment that ensures the quality and safety of the food served to customers. The restaurant management takes food safety seriously and has established standard operating procedures for the regular inspection and maintenance of the wearable equipment like gloves and head caps.

Solution

The restaurant had a CCTV camera setup already installed. The company which provides computer vision solutions captured video samples of people while wearing gloves and hats along with footage of people working without this equipment. The model was trained to detect wearable equipment with high accuracy and log the time and name of the staff member who was not complying with the rules. This log was maintained by the management of the restaurant for efficient monitoring of food safety standards to maintain quality. The restaurant management also initiated a training program for the kitchen staff on the importance of regular equipment checks and the significance of maintaining a safe and hygienic kitchen environment accordingly.

Conclusion

The incident highlighted the importance of regular food safety equipment checks and the need for swift action when any issues arise. The restaurant management’s prompt action ensured that no customers were put at risk, and the restaurant’s reputation for food safety was preserved. Automated equipment checks can help prevent future incidents and maintain a safe and hygienic kitchen environment, ensuring that customers enjoy their dining experience without any health concerns.

Background

Smart parking systems have become increasingly popular over the years, as they help to reduce congestion and save time. A multi-storey car parking facility in the heart of the city was facing a severe parking problem where hundreds of cars were parked every day and the parking usually became full. People who wanted to park their cars had no way of knowing if the parking was full at any given time. The parking facility administration was looking for a solution to improve the parking experience for citizens by counting the number of cars entering and exiting the car park. They turned to an AI-based smart parking system to address these issues.

Solution

The solution used Computer Vision algorithms on the existing CCTV camera setup to analyze the video feeds in the parking area. The solution could identify and count the license plates of vehicles entering and leaving the parking lot. The system used this information to keep track of the number of available spots in the parking lot. The solution used machine learning algorithms to predict the availability of parking spots and display the number of available spots at the parking entrance. The algorithms could analyze historical data on parking usage and predict parking demand at different times of the day. This information was used to optimize the use of parking spaces and reduce waiting times for drivers.

Result

The AI-based smart parking system was a success. Drivers could know about the availability of parking spots without driving around the parking lot. This saved time and reduced the amount of fuel consumed by cars. Our video analytics solution also helped the parking lot administration to generate revenue from the parking lot. The system could accurately record the number of vehicles entering and leaving the parking lot, and the parking fees collected.

Conclusion

The AI-based smart parking system was an effective video analytics solution to the parking problem. The solution helped reduce traffic congestion and improve the parking experience for citizens. It could be replicated in other parking facilities facing similar parking problems to achieve similar results.

Service Category

Web-based application with real-time video monitoring using Yolo5s.

Background

The organization is a medium-sized IT company with approximately 450 employees. The company faced challenges in tracking employee attendance, managing time off requests, and ensuring the security of its facility. The company’s previous time and attendance tracking system relied on manual data entry through biometric machines, which was prone to errors and was time-consuming

Solution

The organization implemented an AI-based time and attendance tracking system with Facial Recognition technology to automate their attendance tracking process. The system utilized advanced algorithms to identify employees based on their facial features, eliminating the need for manual data entry. Employees were required to register their faces in the system, which took only a few seconds. Once registered, the system would recognize employees as they entered the workplace, and their attendance would automatically be recorded. The system also allowed employees to request time off and view their attendance records through a web app.

Result

The AI-based time and attendance tracking system with Facial Recognition technology had a significant impact on the organization. The system provided the following benefits:

Conclusion

The AI-based time and attendance tracking system streamlined their attendance tracking process, improved security, and enhanced employee productivity. As more organizations adopt AI based video analytics systems, we can expect to see an increase in efficiency and accuracy in the workplace.