Cooperative Object Detection and Parameter Estimation Using Visible Light Communications

• URL: http://arxiv.org/abs/2003.07525v1
• Date: Tue, 17 Mar 2020 04:40:33 GMT
• Title: Cooperative Object Detection and Parameter Estimation Using Visible Light Communications
• Authors: Hamid Hosseinianfar, Maite Brandt-Pearce
• Abstract summary: Visible light communication systems are promising candidates for future indoor access and peer-to-peer networks. The performance of these systems is vulnerable to the line of sight (LOS) link blockage due to objects inside the room. We develop a probabilistic object detection method that takes advantage of the blockage status of the LOS links.
• Score: 0.40392458786263447
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visible light communication (VLC) systems are promising candidates for future indoor access and peer-to-peer networks. The performance of these systems, however, is vulnerable to the line of sight (LOS) link blockage due to objects inside the room. In this paper, we develop a probabilistic object detection method that takes advantage of the blockage status of the LOS links between the user devices and transceivers on the ceiling to locate those objects. The target objects are modeled as cylinders with random radii. The location and size of an object can be estimated by using a quadratic programming approach. Simulation results show that the root-mean-squared error can be less than $1$ cm and $8$ cm for estimating the center and the radius of the object, respectively.

Related papers

• Assisting Blind People Using Object Detection with Vocal Feedback [0.0]
  The proposed approach suggests detection of objects in real-time video by using a web camera. The OpenCV libraries of Python is used to implement the software program. Image recognition results are transferred to the visually impaired users in audible form by means of Google text-to-speech library.
  arXiv  Detail & Related papers  (2023-12-18T19:28:23Z)
• PointOBB: Learning Oriented Object Detection via Single Point Supervision [55.88982271340328]
  This paper proposes PointOBB, the first single Point-based OBB generation method, for oriented object detection. PointOBB operates through the collaborative utilization of three distinctive views: an original view, a resized view, and a rotated/flipped (rot/flp) view. Experimental results on the DIOR-R and DOTA-v1.0 datasets demonstrate that PointOBB achieves promising performance.
  arXiv  Detail & Related papers  (2023-11-23T15:51:50Z)
• Weakly-supervised Contrastive Learning for Unsupervised Object Discovery [52.696041556640516]
  Unsupervised object discovery is promising due to its ability to discover objects in a generic manner. We design a semantic-guided self-supervised learning model to extract high-level semantic features from images. We introduce Principal Component Analysis (PCA) to localize object regions.
  arXiv  Detail & Related papers  (2023-07-07T04:03:48Z)
• Multi-View Keypoints for Reliable 6D Object Pose Estimation [12.436320203635143]
  We propose a novel multi-view approach to combine heatmap and keypoint estimates into a probability density map over 3D space. We demonstrate an average pose estimation error of approximately 0.5mm and 2 degrees across a variety of difficult low-feature and reflective objects.
  arXiv  Detail & Related papers  (2023-03-29T16:28:11Z)
• Performance Study of YOLOv5 and Faster R-CNN for Autonomous Navigation around Non-Cooperative Targets [0.0]
  This paper discusses how the combination of cameras and machine learning algorithms can achieve the relative navigation task. The performance of two deep learning-based object detection algorithms, Faster Region-based Convolutional Neural Networks (R-CNN) and You Only Look Once (YOLOv5) is tested. The paper discusses the path to implementing the feature recognition algorithms and towards integrating them into the spacecraft Guidance Navigation and Control system.
  arXiv  Detail & Related papers  (2023-01-22T04:53:38Z)
• PROB: Probabilistic Objectness for Open World Object Detection [15.574535196804042]
  Open World Object Detection (OWOD) is a new computer vision task that bridges the gap between classic object detection (OD) benchmarks and object detection in the real world. We introduce a novel probabilistic framework for objectness estimation, where we alternate between probability distribution estimation and objectness likelihood of known objects. The resulting Probabilistic Objectness transformer-based open-world detector, PROB, integrates our framework into traditional object detection models.
  arXiv  Detail & Related papers  (2022-12-02T20:04:24Z)
• Robust Change Detection Based on Neural Descriptor Fields [53.111397800478294]
  We develop an object-level online change detection approach that is robust to partially overlapping observations and noisy localization results. By associating objects via shape code similarity and comparing local object-neighbor spatial layout, our proposed approach demonstrates robustness to low observation overlap and localization noises.
  arXiv  Detail & Related papers  (2022-08-01T17:45:36Z)
• AdaCon: Adaptive Context-Aware Object Detection for Resource-Constrained Embedded Devices [2.5345835184316536]
  Convolutional Neural Networks achieve state-of-the-art accuracy in object detection tasks. They have large computational and energy requirements that challenge their deployment on resource-constrained edge devices. In this paper, we leverage the prior knowledge about the probabilities that different object categories can occur jointly to increase the efficiency of object detection models. Our experiments using COCO dataset show that our adaptive object detection model achieves up to 45% reduction in the energy consumption, and up to 27% reduction in the latency, with a small loss in the average precision (AP) of object detection.
  arXiv  Detail & Related papers  (2021-08-16T01:21:55Z)
• Slender Object Detection: Diagnoses and Improvements [74.40792217534]
  In this paper, we are concerned with the detection of a particular type of objects with extreme aspect ratios, namely textbfslender objects. For a classical object detection method, a drastic drop of $18.9%$ mAP on COCO is observed, if solely evaluated on slender objects.
  arXiv  Detail & Related papers  (2020-11-17T09:39:42Z)
• Risk-Averse MPC via Visual-Inertial Input and Recurrent Networks for Online Collision Avoidance [95.86944752753564]
  We propose an online path planning architecture that extends the model predictive control (MPC) formulation to consider future location uncertainties. Our algorithm combines an object detection pipeline with a recurrent neural network (RNN) which infers the covariance of state estimates. The robustness of our methods is validated on complex quadruped robot dynamics and can be generally applied to most robotic platforms.
  arXiv  Detail & Related papers  (2020-07-28T07:34:30Z)
• A Self-Training Approach for Point-Supervised Object Detection and Counting in Crowds [54.73161039445703]
  We propose a novel self-training approach that enables a typical object detector trained only with point-level annotations. During training, we utilize the available point annotations to supervise the estimation of the center points of objects. Experimental results show that our approach significantly outperforms state-of-the-art point-supervised methods under both detection and counting tasks.
  arXiv  Detail & Related papers  (2020-07-25T02:14:42Z)

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.