Multicam human tracking AI

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643 Pham Van Dong, Hanoi, Vietnam

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+84 24 7109 9234

Partner: Vietnam National University of Engineering AI Research Lab (UET AI Research Lab)

Project Overview: Our researchers has collaborated with the Vietnam National University of Engineering AI Research Lab (UET AI Research Lab) to develop a state-of-the-art AI Multicam Tracking System. This innovative project focuses on mapping humans captured on camera to a map in real-time. By leveraging advanced computer vision, AI technologies, MLOps techniques, and powerful computing resources like Nvidia DGX, the multicam tracking system delivers accurate and efficient tracking capabilities, suitable for a wide range of applications in security, retail, and urban planning.

Project Objectives:

Develop a multicam tracking system that can accurately map humans captured on camera to a real-time map.
Collaborate with UET AI Research Lab to leverage their expertise in AI and computer vision technologies.
Implement MLOps techniques to streamline the development, training, and deployment of AI models.
Utilize Nvidia DGX for accelerated training and processing of the tracking system.
Create a versatile system that can be used in various industries, such as security, retail, and urban planning.

Techniques and Technologies Used:

Computer Vision: Advanced algorithms and techniques are employed for detecting, tracking, and mapping humans in camera feeds.
Deep Learning: Neural networks are utilized to enhance the accuracy and efficiency of the multicam tracking system.
MLOps: A set of practices for collaboration and communication between data scientists and operations professionals to manage the machine learning lifecycle, streamlining the development, training, and deployment of AI models.
Nvidia DGX: A powerful AI computing platform used for accelerated training and processing of the deep learning models and real-time tracking.
Multi-Camera Calibration: Techniques for calibrating multiple cameras in a single environment to ensure accurate tracking and mapping.
Data Fusion: Combining data from multiple cameras to create a comprehensive and accurate real-time map of human locations.
Python: The primary programming language used for implementing the algorithms and backend systems.

Project Outcomes and Benefits: The AI Multicam Tracking System offers several advantages, including:

Enhanced security: The system can be used for monitoring and securing premises, tracking suspicious activity, and providing real-time alerts.
Improved retail analytics: The system enables retailers to gain insights into customer behavior, foot traffic patterns, and store layout effectiveness.
Urban planning and management: By tracking and analyzing human movement, the system can provide valuable data for optimizing public spaces, transportation networks, and infrastructure.
Scalability: The system can accommodate various camera configurations and can be scaled up or down to suit different applications and environments.
Real-time mapping: The ability to map humans captured on camera to a real-time map allows for prompt decision-making and efficient resource allocation.
Streamlined AI development: The implementation of MLOps techniques ensures efficient collaboration between teams, accelerating the development, training, and deployment of AI models.
Accelerated performance: Utilizing Nvidia DGX for training and processing enhances the system’s performance and enables faster, more accurate tracking.

The AI Multicam Tracking System is a testament to the successful collaboration between our researchers and the UET AI Research Lab. This innovative solution, backed by cutting-edge technologies and MLOps practices, has the potential to transform industries and improve the way organizations monitor and analyze human movement.