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You will be redirected to our submission process. Multi-sensor image fusion focuses on processing images of the same object or scene acquired by multiple sensors, in which various sensors with ...

The aim of the project is to build a multi-sensor localization and mapping system. Its contents are related to the below toppics: sensor assembling sensor testing sychonization and calibration dataset ...

You will be redirected to our submission process. This Research Topic is Volume II of a series. The previous volume can be found here: Multi-Sensor Imaging and Fusion: Methods, Evaluations, and ...

The topics covered in the book include: • AI and deep learning for AVS • Autonomous steering through deep neural networks • Adversarial attacks and defenses on autonomous vehicles • Gesture ...

The scope evolved quite a lot, with the concept being to create a learning platform for environmental multi-sensor fusion. The current cut-down development kit hosts just the air quality ...

The multi-sensor fusion module fuses the output results of Lidar, Camera, and Radar multiple sensors to make the detection results more reliable. It uses post-processing technology, the algorithm used ...

However, traditional track-to-track fusion (T2TF) methods struggle in scenarios involving highly maneuvering targets and collaborative multi-sensor systems with asynchronous and incomplete data. These ...

A higher requirement for roadside perception capabilities is therefore proposed. The roadside multi-sensor data fusion system (RMDFS) can provide new functionalities which will contribute to enhancing ...

Multisensor data fusion is a method of acquiring data and information from more than one sensor and then integrating the acquired information for decision-making rather than relying on single-sensor ...

“CEVA’s SP500 DSP delivers superior real-time performance for computer vision, AI-based analytics and multi-sensing sensor fusion at the edge, ensuring our customers have a powerful, flexible edge AI ...