Hardware: NVIDIA Jetson Xavier NX
Application: Object Tracking for Multiple Categories across Cameras
Industry: Smart Transportation
Deployment Location: US
For urban transportation, managing traffic flow and ensuring road safety has become an increasingly complex task. With the rapid growth of vehicles on the road and the emergence of diverse transportation modes, traditional traffic monitoring systems are struggling to keep pace. It’s really important to not only detect different types of objects on the road in real time but also track their movements seamlessly in the most intricate traffic scenarios.
Identifying and categorizing various types of vehicles, from cars and trucks to bicycles and scooters, is an intricate task. Moreover, distinguishing between pedestrians, ensuring their safety, and monitoring their movements is equally crucial. Traditional methods struggle to provide the accuracy and speed required for these tasks, often leading to delays in incident response and traffic management.
What’s more, coordinating data from multiple analytics cameras spread across the complex road network presents a logistical nightmare. Manual monitoring is not only labor-intensive but also prone to errors. To compound these issues, achieving fast and accurate continuous tracking in such a dynamic environment is fraught with industrial problems such as dealing with numerous detected targets, mutual occlusion, image distortion, background clutter, large differences in viewing angles, small targets, and fast motion speeds. Therefore, the pressing need lies in harnessing the power of computer vision to automate these processes, enabling real-time, high-precision detection and tracking of objects across diverse camera feeds.
The system is a versatile and robust tracking system designed to address the intricacies of various target tracking scenarios continuously, with a special focus on enhancing the capabilities of the NVIDIA Jetson Xavier NX.
Single-category target tracking under a single lens serves as the foundation for continuous tracking of multiple targets of the same category. It employs a lighter backbone network HRNetV2-W18 to ensure high-precision tracking while optimizing computational efficiency. As for Multi-categories detection, its tracking category spans ten different targets, including individuals, bicycles, cars, trucks, buses, and tricycles, demonstrating its proficiency in simultaneously tracking diverse object types.
For the complex scene, we all know security scenarios often demand continuous tracking of a target object across multiple shots. However, transitioning between different camera lenses or scenes frequently leads to target loss. this system addresses the challenge with an effective and swift cross-shot tracking algorithm. This capability is founded on the DeepSORT algorithm, utilizing the detection and ReID models. These components are seamlessly integrated with a trajectory fusion algorithm to maintain both high-performance levels and pinpoint tracking accuracy. Regardless of lens switches or scene changes, it excels in closely following the target, ensuring that the object remains accurately tracked.
Follow our wiki to find out how to deploy Jetson Xavier NX across multiple cameras for continuous tracking!
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