Perspective Aware Road Obstacle Detection

• URL: http://arxiv.org/abs/2210.01779v2
• Date: Mon, 19 Jun 2023 18:29:05 GMT
• Title: Perspective Aware Road Obstacle Detection
• Authors: Krzysztof Lis, Sina Honari, Pascal Fua, Mathieu Salzmann
• Abstract summary: 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.
• Score: 104.57322421897769
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While road obstacle detection techniques have become increasingly effective, they typically ignore the fact that, in practice, the apparent size of the obstacles decreases as their distance to the vehicle increases. In this paper, we account for 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 (i) generate training data by injecting onto the road synthetic objects whose size corresponds to the perspective foreshortening; and (ii) incorporate perspective information in the decoding part of the detection network to guide the obstacle detector. Our results on standard benchmarks show that, together, these two strategies significantly boost the obstacle detection performance, allowing our approach to consistently outperform state-of-the-art methods in terms of instance-level obstacle detection.

Related papers

• Uncertainty Estimation for 3D Object Detection via Evidential Learning [63.61283174146648]
  We introduce a framework for quantifying uncertainty in 3D object detection by leveraging an evidential learning loss on Bird's Eye View representations in the 3D detector. We demonstrate both the efficacy and importance of these uncertainty estimates on identifying out-of-distribution scenes, poorly localized objects, and missing (false negative) detections.
  arXiv  Detail & Related papers  (2024-10-31T13:13:32Z)
• Cut-and-Paste with Precision: a Content and Perspective-aware Data Augmentation for Road Damage Detection [5.939858158928473]
  Road damage can pose significant challenges to the integrity, safety, and durability of road infrastructure. In recent years, researchers have explored various data-driven methods for image-based damage detection in road monitoring applications. We propose an improved Cut-and-paste augmentation technique that is both content-aware (i.e. considers the true location of the road in the image) and perspective-aware (i.e. takes into account the difference in perspective between the injected damage and the target image)
  arXiv  Detail & Related papers  (2024-06-06T09:06:42Z)
• OOSTraj: Out-of-Sight Trajectory Prediction With Vision-Positioning Denoising [49.86409475232849]
  Trajectory prediction is fundamental in computer vision and autonomous driving. Existing approaches in this field often assume precise and complete observational data. We present a novel method for out-of-sight trajectory prediction that leverages a vision-positioning technique.
  arXiv  Detail & Related papers  (2024-04-02T18:30:29Z)
• Improving Online Lane Graph Extraction by Object-Lane Clustering [106.71926896061686]
  We propose an architecture and loss formulation to improve the accuracy of local lane graph estimates. The proposed method learns to assign the objects to centerlines by considering the centerlines as cluster centers. We show that our method can achieve significant performance improvements by using the outputs of existing 3D object detection methods.
  arXiv  Detail & Related papers  (2023-07-20T15:21:28Z)
• Rigidity-Aware Detection for 6D Object Pose Estimation [60.88857851869196]
  Most recent 6D object pose estimation methods first use object detection to obtain 2D bounding boxes before actually regressing the pose. We propose a rigidity-aware detection method exploiting the fact that, in 6D pose estimation, the target objects are rigid. Key to the success of our approach is a visibility map, which we propose to build using a minimum barrier distance between every pixel in the bounding box and the box boundary.
  arXiv  Detail & Related papers  (2023-03-22T09:02:54Z)
• Spatio-Temporal Context Modeling for Road Obstacle Detection [12.464149169670735]
  A data-driven context-temporal model of the driving scene is constructed with the layouts of the training data. Obstacles are detected via state-of-the-art object detection algorithms, and the results are combined with the generated scene.
  arXiv  Detail & Related papers  (2023-01-19T07:06:35Z)
• Real-Time Accident Detection in Traffic Surveillance Using Deep Learning [0.8808993671472349]
  This paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications. The proposed framework consists of three hierarchical steps, including efficient and accurate object detection based on the state-of-the-art YOLOv4 method. The robustness of the proposed framework is evaluated using video sequences collected from YouTube with diverse illumination conditions.
  arXiv  Detail & Related papers  (2022-08-12T19:07:20Z)
• Improved detection of small objects in road network sequences [0.0]
  We propose a new procedure for detecting small-scale objects by applying super-resolution processes based on detections performed by convolutional neural networks. This work has been tested for a set of traffic images containing elements of different scales to test the efficiency according to the detections obtained by the model.
  arXiv  Detail & Related papers  (2021-05-18T10:13:23Z)
• 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)
• Detecting Lane and Road Markings at A Distance with Perspective Transformer Layers [5.033948921121557]
  In existing approaches, the detection accuracy often degrades with the increasing distance. This is due to the fact that distant lane and road markings occupy a small number of pixels in the image. Inverse Perspective Mapping can be used to eliminate the perspective distortion, but the inherent can lead to artifacts.
  arXiv  Detail & Related papers  (2020-03-19T03:22:52Z)

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.