The client's business involves managing shipyards where monitoring loading and unloading activities is crucial for operational efficiency and safety. To accomplish this, they installed multiple cameras across the shipyard premises. However, due to the diverse layout of the shipyards and the varying perspectives required for comprehensive monitoring, these cameras were placed at different locations and angles.
The client struggled to accurately monitor loading and unloading activities in shipyards due to the non-fixed positions of multiple cameras. This variability hampered consistent angle views and made activity counting challenging. They sought technology solutions to analyze video feeds, compensate for angle variations, and provide precise activity counts. Integration into existing workflows was essential for seamless deployment. By addressing these issues, the client aimed to achieve accurate monitoring despite camera mobility.
To address the client’s problem, we developed an adaptable algorithm using Python, GPU server infrastructure, PyTorch, and YOLO technology. This algorithm dynamically adjusts to different camera angles and positions, ensuring consistent and accurate counting of loading and unloading activities in real-time. By employing deep learning techniques, particularly YOLO for object detection, the solution efficiently processes data from multiple cameras without the need for fixed camera positions.
The algorithm adjusts to different camera angles and positions, providing consistent and reliable counting regardless of variations in the environment.
Enables seamless deployment on the client’s in-house server, optimizing computational resources for efficient processing.
Can scale to accommodate additional cameras or changes in the shipyard layout without compromising accuracy or performance.
Python, GPU server, PyTorch, YOLO
