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Human Pose Estimation and Movement Prediction

In today's digital era, the ability to understand and predict human movements can transform how we interact with technology. The Human Pose Estimation and Movement Prediction Tool brings this vision to life by offering an excellent approach to analyzing human body language. By harnessing the power of advanced computer vision and machine learning, our solution not only detects but interprets the intricacies of human body positions.

Developed in Python, this tool stands out for its flexibility and robust performance. We utilize the latest in OpenCV technology to ensure our image processing is both efficient and effective. Designed to be universally compatible, our software can seamlessly integrate with any operating system that supports Python and OpenCV, making it a versatile choice for developers looking to elevate their applications.

This capability allows for an anticipation of actions, enabling a more responsive and intuitive interaction with applications. Imagine the possibilities when your systems can predict the next move in near real-time or real-time, enhancing user experience and opening new avenues for innovation across various industries.

The HPE&MP Tool accepts both images and streaming video as input, processing this data to detect human figures. Upon detection, it overlays an exoskeleton representation of the body’s pose on the feed. This visual output not only provides clear insights into the current posture but also predicts future movements, offering a dynamic tool for a wide range of applications, from enhancing security measures to creating more immersive gaming experiences.


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Features Extraction

The system processes both images and streaming video, providing outputs that clearly highlight detected features within the frame for immediate action.

Designed to identify and isolate critical patterns within images and videos, enabling detailed analysis across various applications. From selectively blurring specific targets to extracting comprehensive details from vehicles, including model, color, and license plate, the software adapts to diverse needs with precision. Leveraging machine learning algorithms trained on specialized datasets, the system ensures accurate feature extraction. This accuracy is crucial for applications requiring fine detail, such as weapon detection and classification.

Python forms the backbone of the technology stack, chosen for its flexibility. Image processing is powered by OpenCV and enhanced with YOLO, specifically adjusted on custom datasets for precise feature identification and classification. This framework ensures compatibility with a wide range of operating systems that accommodate the necessary software versions, allowing for broad application.


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Target Tracking

The Target Tracking Algorithm, adaptable for use with both drone-mounted sensors and fixed cameras, is especially designed for surveillance and tracking tasks. It excels in complex environments where precision, reliability, and real-time analysis are mandatories. By recognizing multiple targets and focusing on selected ones without losing track of others, our software offers unparalleled monitoring capabilities.

At the core of our platform is a robust foundation built on Python, optimized for seamless integration into existing systems. Our advanced image analysis and object detection capabilities are powered by OpenCV, with enhanced precision and performance provided by the integration of YOLO via TensorFlow. This combination ensures efficient processing of images or video streams, enabling the accurate detection and tracking of targets.

Machine Learning Architectures: Developed using extensive datasets, including over 8000 manually labeled images for multicopters tracking and over 6000 manually labeled images for military vehicles, our ML architectures are fine-tuned for specific applications, from drone path prediction to vessel and military vehicle monitoring.

Compatibility and Integration: Designed for flexibility, our solution is compatible with a broad range of operating systems that support the required technological stack, ensuring easy integration into diverse operational environments.

Key features

Multicopter Tracking: Development of a ML (Machine Learning) Architecture, based on a Multicopter Dataset of 8000+ manually labeled images, to determine the presence of a drone and following his path, enabling the application of movement prediction and flight counter measures.

Vessel Tracking: Development of a ML Architecture, based on a dataset composed of several types of classified vessel, including warships. This feature enhances the Computer Vision capacity and enables the Moving Platform Landing.

Military Vehicle Tracking: Development of a ML Architecture, based on the most common military vehicle with a particular focus on tanks (as of now the dataset is composed of 6000+ manually labeled images), to provide the detection of the vehicle and enable path prediction algorithms.


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Target Localization

The Target Localization Toolbox is designed to enhance the precision of GNSS coordinate tracing across diverse environments. This innovative solution integrates the satellite positioning systems and computer vision technologies through sensor fusion, delivering real-time target tracking with exceptional accuracy.

Configurable to work with various devices, such as gimbals or radar systems, it introduces an efficient way to localize targets immediately upon detection. It is also possible to apply Geospatial Calculations using angular information provided by the gimbal along with the data from the onboard sensors to pinpoint the target’s location in three-dimensional space.

The Target Localization Toolbox is adaptable to a variety of geodetic datums, including the World Geodetic System 1984 (WGS84), European Terrestrial Reference System 1989 (ETRS89), North American Datum 1983 (NAD83), and the Earth Gravitational Model 2008 (EGM2008).
Built on a foundation of Python and C++, our technology stack is equipped with OpenCV for image processing, the Geospatial Data Abstraction Library (GDAL) for geospatial analysis, and OpenMP for enhanced parallel processing capabilities. This combination ensures that our solution is not only powerful but also versatile, capable of deployment across any operating system that meets the specified requirements.


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Multi-Target Recognition

The Multi-Target Recognition Toolbox is a sophisticated software solution designed to redefine the approach to real-time detection and classification of multiple targets. This advanced tool combines the strengths of computer vision, deep learning, and pattern recognition algorithms to deliver a comprehensive system for enhancing situational awareness, improving safety, and facilitating informed decision-making. With its ability to process and analyze complex environments, this toolbox stands as an indispensable asset for a wide range of applications.

This system, primarily programmed in Python, is engineered to support the intricate demands of advanced target recognition, utilizing OpenCV for sophisticated image analysis and a proprietary neural network architecture for precise target classification. The solution is designed for compatibility with a variety of major operating systems that support Python, OpenCV, and YOLO frameworks, ensuring flexible integration into different operational setups.

 

The toolbox processes input in the form of images or video streams, employing its advanced algorithms to detect and classify multiple targets within a frame. Once a target is identified, the system highlights these elements, providing clear and actionable insights. This feature is instrumental in environments where accurate and timely information is crucial.


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Target Prioritization

A sophisticated solution designed to redefine the approach to target analysis. Utilizing artificial intelligence, this system assigns significance or ranks to detected targets by evaluating data and context in real-time, based on predefined criteria. This process enables users to swiftly identify and focus on high-priority targets, such as potential threats in a security feed, by ranking them according to the level of urgency or preference. The capability to differentiate and prioritize among various detected objects, including vehicles and individuals, streamlines response strategies and optimizes resource deployment.

The backbone of the Target Prioritization Toolbox is Python, complemented by the image processing prowess of OpenCV and the object detection capabilities of YOLO, all integrated using TensorFlow. This powerful combination ensures that the toolbox not only performs with exceptional accuracy but also integrates smoothly with various operational ecosystems. The system is engineered to be fully compatible across a wide range of operating systems that support the latest versions of Python, OpenCV, and YOLO, facilitating effortless implementation and scalability.

 

The toolbox processes both images and video streams, applying its advanced algorithms to highlight detected targets based on a customizable priority scale. This prioritization allows for immediate focus on critical threats or objects, enhancing situational awareness and decision-making efficiency.


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Environmental Mapping

Inputs such as images, streaming video, and data from onboard sensors like IMU, GPS, ToF, and LiDAR are processed to generate a Cloud Point Graph, offering a detailed and scalable representation of the environment. This approach not only enhances the accuracy of digital maps but also ensures that users have access to the most detailed environmental data possible.

The Environmental Mapping Toolbox is a cutting-edge solution designed to transform sensor data into detailed 3D maps of any surroundings. Utilizing advanced sensors such as monocular or stereoscopic cameras, sonar and LiDAR, our technology meticulously analyzes and interprets data to construct high-fidelity representations of environments. This process includes the accurate depiction of obstacles, terrain variations, and built structures, enabling a comprehensive understanding of any physical space.

This Toolbox is engineered with a robust combination of Python and C/C++, integrating the analytical power of OpenCV with sophisticated SLAM (Simultaneous Localization and Mapping) techniques for precise environmental mapping. Further enhanced by compatibility with ROS and open and/or customs FCS firmware, the system offers a broad spectrum of functionality and versatility for diverse mapping needs. This toolbox is designed for universal application, supporting all major operating systems capable of running Python and C/C++.


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Red Teaming

The Red Teaming solution is designed to proactively challenge and enhance the resilience of existing systems. Our approach goes beyond simple detection by offering applicable countermeasures and conducting thorough research to uncover possible vulnerabilities, weaknesses, and areas for improvement in existing systems. Through meticulous analysis, our methodology not only identifies areas for improvement but also sets the stage for the development of advanced countermeasures. Through strategic deception techniques, we also aim to outmaneuver AI algorithms, addressing and mitigating identified vulnerabilities with precision.

Utilizing Python and C/C++, our red teaming process involves an intricate mathematical framework that scrutinizes machine learning architectures and computer vision algorithms. This rigorous workflow facilitates the thorough identification of system frailties and the crafting of sophisticated strategies to exploit and subsequently neutralize these vulnerabilities. This proactive approach ensures the enhancement of system robustness, preparing them to counter advanced threats effectively.

Inputs such as images, streaming video, and data from onboard sensors are analyzed to simulate and test complex scenarios, including drone swarms in automatic or semiautomatic modes, showcasing advanced flying formations and tactics. This process not only tests the system’s resilience but also its adaptability to evolving threat landscapes.


Our Products purchases include all the necessary documentation, a bulk hours of customer training and a comprehensive 24/7 after-sale assistance and pre-planned maintenance. Contact us today to book a flight demonstration!


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