Decisive Data using Multi-Modality Optical Sensors for Advanced Vehicular Systems

• URL: http://arxiv.org/abs/2307.13600v1
• Date: Tue, 25 Jul 2023 16:03:47 GMT
• Title: Decisive Data using Multi-Modality Optical Sensors for Advanced Vehicular Systems
• Authors: Muhammad Ali Farooq, Waseem Shariff, Mehdi Sefidgar Dilmaghani, Wang Yao, Moazam Soomro, and Peter Corcoran
• Abstract summary: This paper focuses on various optical technologies for design and development of state-of-the-art out-cabin forward vision systems and in-cabin driver monitoring systems. The focused optical sensors include Longwave Thermal Imaging (LWIR) cameras, Near Infrared (NIR), Neuromorphic/ event cameras, Visible CMOS cameras and Depth cameras.
• Score: 1.3315340349412819
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Optical sensors have played a pivotal role in acquiring real world data for critical applications. This data, when integrated with advanced machine learning algorithms provides meaningful information thus enhancing human vision. This paper focuses on various optical technologies for design and development of state-of-the-art out-cabin forward vision systems and in-cabin driver monitoring systems. The focused optical sensors include Longwave Thermal Imaging (LWIR) cameras, Near Infrared (NIR), Neuromorphic/ event cameras, Visible CMOS cameras and Depth cameras. Further the paper discusses different potential applications which can be employed using the unique strengths of each these optical modalities in real time environment.

Related papers

• MAROON: A Framework for the Joint Characterization of Near-Field High-Resolution Radar and Optical Depth Imaging Techniques [4.816237933371206]
  We take on the unique challenge of characterizing depth imagers from both, the optical and radio-frequency domain. We provide a comprehensive evaluation of their depth measurements with respect to distinct object materials, geometries, and object-to-sensor distances. All object measurements will be made public in form of a multimodal dataset, called MAROON.
  arXiv  Detail & Related papers  (2024-11-01T11:53:10Z)
• Event-based vision on FPGAs -- a survey [0.0]
  Field programmable gate Arrays (FPGAs) have enabled fast data processing (even in real-time) and energy efficiency. This paper gives an overview of the most important works, where FPGAs have been used in different contexts to process event data. It covers applications in the following areas: filtering, stereovision, optical flow, acceleration of AI-based algorithms for object classification, detection and tracking, and applications in robotics and inspection systems.
  arXiv  Detail & Related papers  (2024-07-11T10:07:44Z)
• DIDLM:A Comprehensive Multi-Sensor Dataset with Infrared Cameras, Depth Cameras, LiDAR, and 4D Millimeter-Wave Radar in Challenging Scenarios for 3D Mapping [7.050468075029598]
  This study presents a comprehensive multi-sensor dataset designed for 3D mapping in challenging indoor and outdoor environments. The dataset comprises data from infrared cameras, depth cameras, LiDAR, and 4D millimeter-wave radar. Various SLAM algorithms are employed to process the dataset, revealing performance differences among algorithms in different scenarios.
  arXiv  Detail & Related papers  (2024-04-15T09:49:33Z)
• Random resistive memory-based deep extreme point learning machine for unified visual processing [67.51600474104171]
  We propose a novel hardware-software co-design, random resistive memory-based deep extreme point learning machine (DEPLM) Our co-design system achieves huge energy efficiency improvements and training cost reduction when compared to conventional systems.
  arXiv  Detail & Related papers  (2023-12-14T09:46:16Z)
• Multi-Modal Dataset Acquisition for Photometrically Challenging Object [56.30027922063559]
  This paper addresses the limitations of current datasets for 3D vision tasks in terms of accuracy, size, realism, and suitable imaging modalities for photometrically challenging objects. We propose a novel annotation and acquisition pipeline that enhances existing 3D perception and 6D object pose datasets.
  arXiv  Detail & Related papers  (2023-08-21T10:38:32Z)
• Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks [55.81577205593956]
  Event cameras are bio-inspired sensors that capture the per-pixel intensity changes asynchronously. Deep learning (DL) has been brought to this emerging field and inspired active research endeavors in mining its potential.
  arXiv  Detail & Related papers  (2023-02-17T14:19:28Z)
• DensePose From WiFi [86.61881052177228]
  We develop a deep neural network that maps the phase and amplitude of WiFi signals to UV coordinates within 24 human regions. Our model can estimate the dense pose of multiple subjects, with comparable performance to image-based approaches.
  arXiv  Detail & Related papers  (2022-12-31T16:48:43Z)
• Simulating single-photon detector array sensors for depth imaging [2.497104612216142]
  Single-Photon Avalanche Detector (SPAD) arrays are a rapidly emerging technology. We establish a robust yet simple numerical procedure that establishes the fundamental limits to depth imaging with SPAD arrays.
  arXiv  Detail & Related papers  (2022-10-07T13:23:34Z)
• Analyzing General-Purpose Deep-Learning Detection and Segmentation Models with Images from a Lidar as a Camera Sensor [0.06554326244334865]
  This work explores the potential of general-purpose DL perception algorithms for processing image-like outputs of advanced lidar sensors. Rather than processing the three-dimensional point cloud data, this is, to the best of our knowledge, the first work to focus on low-resolution images with 360text field of view. We show that with adequate preprocessing, general-purpose DL models can process these images, opening the door to their usage in environmental conditions.
  arXiv  Detail & Related papers  (2022-03-08T13:14:43Z)
• TUM-VIE: The TUM Stereo Visual-Inertial Event Dataset [50.8779574716494]
  Event cameras are bio-inspired vision sensors which measure per pixel brightness changes. They offer numerous benefits over traditional, frame-based cameras, including low latency, high dynamic range, high temporal resolution and low power consumption. To foster the development of 3D perception and navigation algorithms with event cameras, we present the TUM-VIE dataset.
  arXiv  Detail & Related papers  (2021-08-16T19:53:56Z)
• Learning Camera Miscalibration Detection [83.38916296044394]
  This paper focuses on a data-driven approach to learn the detection of miscalibration in vision sensors, specifically RGB cameras. Our contributions include a proposed miscalibration metric for RGB cameras and a novel semi-synthetic dataset generation pipeline based on this metric. By training a deep convolutional neural network, we demonstrate the effectiveness of our pipeline to identify whether a recalibration of the camera's intrinsic parameters is required or not.
  arXiv  Detail & Related papers  (2020-05-24T10:32:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.