Vehicle Detection, Tracking, and Counting in a Leading Steel Factory: Enhancing Security and surveillance efficiency.
In a renowned steel factory in India, the efficient management of incoming and outgoing vehicles is crucial for maintaining operational effectiveness, ensuring security, and complying with safety regulations. The factory has been facing challenges in monitoring and tracking the influx of trucks carrying coal and other raw materials. The previous process was to maintain a bunch of paper sheets to manually keep track of those vehicles along with their license plate numbers and submit a report to the authority. To address this issue, the management decided to implement a comprehensive Vehicle Detection, Tracking, and Counting (VDTC) system. The primary objective of this case study is to provide an overview of how we implement the VDTC system and its impact on the factory's operations.
Objectives
The key objectives of implementing the VDTC system in the steel factory are as follows
Accurately detect and classify different types of vehicles, including trucks, entering and exiting the factory premises.
Capture license plate numbers of all vehicles passing through the designated checkpoints.
Count the number of vehicles both incoming and outgoing.
Monitor the movement of vehicles within the factory and track their entry and exit times.
Generate comprehensive reports that include the number plates and images of trucks entering and exiting the factory
Challenges
Steel factories often have harsh environmental conditions, including dust, smoke, and extreme temperatures.
Fluctuating lighting conditions, especially during nighttime and adverse weather conditions, posed challenges for accurate vehicle detection and license plate recognition.
Integrating our system with the factory's existing security and monitoring infrastructure was complex.
Accurately classifying vehicles based on size and type, especially in a factory with various vehicle sizes and shapes, was challenging.
Avoiding false alarms, where non-vehicles were mistakenly detected as vehicles, was critical to prevent unnecessary disruptions and resource wastage.
Solutions
We have collaborated with an installation partner to strategically place multiple surveillance cameras at key entry and exit points within the factory premises. These cameras have a clear view of vehicles entering and exiting.
We have also installed a dedicated AI server within the factory premises to process video streams from the surveillance cameras. This local server ensures real-time processing and reduces the dependence on external cloud services, enhancing data security and minimizing latency.
We have deployed our readily available Vision A Eye module for vehicle tracking, counting and license plate reading into the on-premises server and connected the cameras for real time video stream processing.
Our AI system automatically generates comprehensive reports summarizing the day's vehicle activity. These reports include vehicle count (incoming and outgoing), images of vehicles, captured license plate numbers.
Outcomes
The system allowed for real-time monitoring of all vehicles entering and exiting the factory, reducing the risk of unauthorized access.
The automated counting and tracking of vehicles eliminated the need for manual record-keeping, saving time and reducing errors.
The technology ensured accurate capture of number plates, reducing the likelihood of data entry errors.
The factory could easily provide accurate records of vehicle entry and exit times, aiding in compliance with safety and environmental regulations.
The collected data enabled the factory to analyze vehicle traffic patterns, helping to optimize logistics and resource allocation.
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Objectives
Solutions
