An Embedded Real-time Object Alert System for Visually Impaired: A Monocular Depth Estimation based Approach through Computer Vision

• URL: http://arxiv.org/abs/2507.08165v1
• Date: Thu, 10 Jul 2025 20:55:22 GMT
• Title: An Embedded Real-time Object Alert System for Visually Impaired: A Monocular Depth Estimation based Approach through Computer Vision
• Authors: Jareen Anjom, Rashik Iram Chowdhury, Tarbia Hasan, Md. Ishan Arefin Hossain,
• Abstract summary: Visually impaired people face significant challenges in their day-to-day commutes in the urban cities of Bangladesh.<n>It is paramount for a system to be developed that can alert the visually impaired of objects at close distance beforehand.<n>The proposed system can alert the individual to objects that are present at a close distance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Visually impaired people face significant challenges in their day-to-day commutes in the urban cities of Bangladesh due to the vast number of obstructions on every path. With many injuries taking place through road accidents on a daily basis, it is paramount for a system to be developed that can alert the visually impaired of objects at close distance beforehand. To overcome this issue, a novel alert system is proposed in this research to assist the visually impaired in commuting through these busy streets without colliding with any objects. The proposed system can alert the individual to objects that are present at a close distance. It utilizes transfer learning to train models for depth estimation and object detection, and combines both models to introduce a novel system. The models are optimized through the utilization of quantization techniques to make them lightweight and efficient, allowing them to be easily deployed on embedded systems. The proposed solution achieved a lightweight real-time depth estimation and object detection model with an mAP50 of 0.801.

Related papers

• Vision-based Perception for Autonomous Vehicles in Obstacle Avoidance Scenarios [0.0]
  We propose an efficient obstacle avoidance pipeline that leverages a camera-only perception module and a Frenet-Pure Pursuit-based planning strategy.<n>By integrating advancements in computer vision, the system utilizes YOLOv11 for object detection and state-of-the-art monocular depth estimation models, such as Depth Anything V2, to estimate object distances.<n>The system is evaluated in diverse scenarios on a university campus, demonstrating its effectiveness in handling various obstacles and enhancing autonomous navigation.
  arXiv  Detail & Related papers  (2025-07-16T17:41:14Z)
• Lane-Wise Highway Anomaly Detection [8.086502588472783]
  This paper proposes a scalable and interpretable framework for lane-wise highway traffic anomaly detection.<n>Unlike traditional sensor-dependent methods, our approach uses AI-powered vision models to extract lane-specific features.<n>Our framework outperforms state-of-the-art methods in precision, recall, and F1-score.
  arXiv  Detail & Related papers  (2025-05-05T12:32:23Z)
• An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras [0.13092499936969584]
  Real time vehicle detection is a challenging task for urban traffic surveillance.<n>Fish eye cameras are widely used in real time vehicle detection purpose to provide large area coverage and 360 degree view at junctions.<n>To overcome challenges such as light glare from vehicles and street lights, shadow, non-linear distortion, scaling issues of vehicles and proper localization of small vehicles, a modified YOLOv5 object detection scheme is proposed.
  arXiv  Detail & Related papers  (2025-02-06T23:42:05Z)
• Floor extraction and door detection for visually impaired guidance [78.94595951597344]
  Finding obstacle-free paths in unknown environments is a big navigation issue for visually impaired people and autonomous robots. New devices based on computer vision systems can help impaired people to overcome the difficulties of navigating in unknown environments in safe conditions. In this work it is proposed a combination of sensors and algorithms that can lead to the building of a navigation system for visually impaired people.
  arXiv  Detail & Related papers  (2024-01-30T14:38:43Z)
• MonoTDP: Twin Depth Perception for Monocular 3D Object Detection in Adverse Scenes [49.21187418886508]
  This paper proposes a monocular 3D detection model designed to perceive twin depth in adverse scenes, termed MonoTDP. We first introduce an adaptive learning strategy to aid the model in handling uncontrollable weather conditions, significantly resisting degradation caused by various degrading factors. Then, to address the depth/content loss in adverse regions, we propose a novel twin depth perception module that simultaneously estimates scene and object depth.
  arXiv  Detail & Related papers  (2023-05-18T13:42:02Z)
• Perspective Aware Road Obstacle Detection [104.57322421897769]
  We show that road obstacle detection techniques ignore the fact that, in practice, the apparent size of the obstacles decreases as their distance to the vehicle increases. We leverage this by computing a scale map encoding the apparent size of a hypothetical object at every image location. We then leverage this perspective map to generate training data by injecting onto the road synthetic objects whose size corresponds to the perspective foreshortening.
  arXiv  Detail & Related papers  (2022-10-04T17:48:42Z)
• Combining Visual Saliency Methods and Sparse Keypoint Annotations to Providently Detect Vehicles at Night [2.0299248281970956]
  We explore the potential saliency-based approaches to create different object representations based on the visual saliency and sparse keypoint annotations. We show that this approach allows for an automated derivation of different object representations. We provide further powerful tools and methods to study the problem of detecting vehicles at night before they are actually visible.
  arXiv  Detail & Related papers  (2022-04-25T09:56:34Z)
• Provident Vehicle Detection at Night for Advanced Driver Assistance Systems [3.7468898363447654]
  We present a complete system capable of providingntly detect oncoming vehicles at nighttime based on their caused light artifacts. We quantify the time benefit that the provident vehicle detection system provides compared to an in-production computer vision system.
  arXiv  Detail & Related papers  (2021-07-23T15:27:17Z)
• Analysis of voxel-based 3D object detection methods efficiency for real-time embedded systems [93.73198973454944]
  Two popular voxel-based 3D object detection methods are studied in this paper. Our experiments show that these methods mostly fail to detect distant small objects due to the sparsity of the input point clouds at large distances. Our findings suggest that a considerable part of the computations of existing methods is focused on locations of the scene that do not contribute with successful detection.
  arXiv  Detail & Related papers  (2021-05-21T12:40:59Z)
• Detecting Invisible People [58.49425715635312]
  We re-purpose tracking benchmarks and propose new metrics for the task of detecting invisible objects. We demonstrate that current detection and tracking systems perform dramatically worse on this task. Second, we build dynamic models that explicitly reason in 3D, making use of observations produced by state-of-the-art monocular depth estimation networks.
  arXiv  Detail & Related papers  (2020-12-15T16:54:45Z)
• SoDA: Multi-Object Tracking with Soft Data Association [75.39833486073597]
  Multi-object tracking (MOT) is a prerequisite for a safe deployment of self-driving cars. We propose a novel approach to MOT that uses attention to compute track embeddings that encode dependencies between observed objects.
  arXiv  Detail & Related papers  (2020-08-18T03:40:25Z)
• Training-free Monocular 3D Event Detection System for Traffic Surveillance [93.65240041833319]
  Existing event detection systems are mostly learning-based and have achieved convincing performance when a large amount of training data is available. In real-world scenarios, collecting sufficient labeled training data is expensive and sometimes impossible. We propose a training-free monocular 3D event detection system for traffic surveillance.
  arXiv  Detail & Related papers  (2020-02-01T04:42:57Z)

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.