Data Fusion of Semantic and Depth Information in the Context of Object Detection

• URL: http://arxiv.org/abs/2412.03490v1
• Date: Wed, 04 Dec 2024 17:26:30 GMT
• Title: Data Fusion of Semantic and Depth Information in the Context of Object Detection
• Authors: Md Abu Yusuf, Md Rezaul Karim Khan, Partha Pratim Saha, Mohammed Mahbubur Rahaman,
• Abstract summary: Region-based Convolution Neural Network (R-CNN) with inception v2 is utilized. Cutting-edge technologies of computer vision algorithms are applied to generate a 3D reference point of the region of interest.
• Score: 0.0
• License:
• Abstract: Considerable study has already been conducted regarding autonomous driving in modern era. An autonomous driving system must be extremely good at detecting objects surrounding the car to ensure safety. In this paper, classification, and estimation of an object's (pedestrian) position (concerning an ego 3D coordinate system) are studied and the distance between the ego vehicle and the object in the context of autonomous driving is measured. To classify the object, faster Region-based Convolution Neural Network (R-CNN) with inception v2 is utilized. First, a network is trained with customized dataset to estimate the reference position of objects as well as the distance from the vehicle. From camera calibration to computing the distance, cutting-edge technologies of computer vision algorithms in a series of processes are applied to generate a 3D reference point of the region of interest. The foremost step in this process is generating a disparity map using the concept of stereo vision.

Related papers

• 3D Object Detection for Autonomous Driving: A Comprehensive Survey [48.30753402458884]
  3D object detection, which intelligently predicts the locations, sizes, and categories of the critical 3D objects near an autonomous vehicle, is an important part of a perception system. This paper reviews the advances in 3D object detection for autonomous driving.
  arXiv  Detail & Related papers  (2022-06-19T19:43:11Z)
• Weakly Supervised Training of Monocular 3D Object Detectors Using Wide Baseline Multi-view Traffic Camera Data [19.63193201107591]
  7DoF prediction of vehicles at an intersection is an important task for assessing potential conflicts between road users. We develop an approach using a weakly supervised method of fine tuning 3D object detectors for traffic observation cameras. Our method achieves vehicle 7DoF pose prediction accuracy on our dataset comparable to the top performing monocular 3D object detectors on autonomous vehicle datasets.
  arXiv  Detail & Related papers  (2021-10-21T08:26:48Z)
• CFTrack: Center-based Radar and Camera Fusion for 3D Multi-Object Tracking [9.62721286522053]
  We propose an end-to-end network for joint object detection and tracking based on radar and camera sensor fusion. Our proposed method uses a center-based radar-camera fusion algorithm for object detection and utilizes a greedy algorithm for object association. We evaluate our method on the challenging nuScenes dataset, where it achieves 20.0 AMOTA and outperforms all vision-based 3D tracking methods in the benchmark.
  arXiv  Detail & Related papers  (2021-07-11T23:56:53Z)
• Learnable Online Graph Representations for 3D Multi-Object Tracking [156.58876381318402]
  We propose a unified and learning based approach to the 3D MOT problem. We employ a Neural Message Passing network for data association that is fully trainable. We show the merit of the proposed approach on the publicly available nuScenes dataset by achieving state-of-the-art performance of 65.6% AMOTA and 58% fewer ID-switches.
  arXiv  Detail & Related papers  (2021-04-23T17:59:28Z)
• Ground-aware Monocular 3D Object Detection for Autonomous Driving [6.5702792909006735]
  Estimating the 3D position and orientation of objects in the environment with a single RGB camera is a challenging task for low-cost urban autonomous driving and mobile robots. Most of the existing algorithms are based on the geometric constraints in 2D-3D correspondence, which stems from generic 6D object pose estimation. We introduce a novel neural network module to fully utilize such application-specific priors in the framework of deep learning.
  arXiv  Detail & Related papers  (2021-02-01T08:18:24Z)
• Achieving Real-Time LiDAR 3D Object Detection on a Mobile Device [53.323878851563414]
  We propose a compiler-aware unified framework incorporating network enhancement and pruning search with the reinforcement learning techniques. Specifically, a generator Recurrent Neural Network (RNN) is employed to provide the unified scheme for both network enhancement and pruning search automatically. The proposed framework achieves real-time 3D object detection on mobile devices with competitive detection performance.
  arXiv  Detail & Related papers  (2020-12-26T19:41:15Z)
• Fine-Grained Vehicle Perception via 3D Part-Guided Visual Data Augmentation [77.60050239225086]
  We propose an effective training data generation process by fitting a 3D car model with dynamic parts to vehicles in real images. Our approach is fully automatic without any human interaction. We present a multi-task network for VUS parsing and a multi-stream network for VHI parsing.
  arXiv  Detail & Related papers  (2020-12-15T03:03:38Z)
• Towards Autonomous Driving: a Multi-Modal 360$^{\circ}$ Perception Proposal [87.11988786121447]
  This paper presents a framework for 3D object detection and tracking for autonomous vehicles. The solution, based on a novel sensor fusion configuration, provides accurate and reliable road environment detection. A variety of tests of the system, deployed in an autonomous vehicle, have successfully assessed the suitability of the proposed perception stack.
  arXiv  Detail & Related papers  (2020-08-21T20:36:21Z)
• PerMO: Perceiving More at Once from a Single Image for Autonomous Driving [76.35684439949094]
  We present a novel approach to detect, segment, and reconstruct complete textured 3D models of vehicles from a single image. Our approach combines the strengths of deep learning and the elegance of traditional techniques. We have integrated these algorithms with an autonomous driving system.
  arXiv  Detail & Related papers  (2020-07-16T05:02:45Z)
• Ego-motion and Surrounding Vehicle State Estimation Using a Monocular Camera [11.29865843123467]
  We propose a novel machine learning method to estimate ego-motion and surrounding vehicle state using a single monocular camera. Our approach is based on a combination of three deep neural networks to estimate the 3D vehicle bounding box, depth, and optical flow from a sequence of images.
  arXiv  Detail & Related papers  (2020-05-04T16:41:38Z)

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.