A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving

• URL: http://arxiv.org/abs/2011.10671v2
• Date: Sun, 11 Jul 2021 07:04:10 GMT
• Title: A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving
• Authors: Di Feng, Ali Harakeh, Steven Waslander, Klaus Dietmayer
• Abstract summary: Capturing uncertainty in object detection is indispensable for safe autonomous driving. There is no summary on uncertainty estimation in deep object detection. This paper provides a review and comparative study on existing probabilistic object detection methods.
• Score: 14.034548457000884
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Capturing uncertainty in object detection is indispensable for safe autonomous driving. In recent years, deep learning has become the de-facto approach for object detection, and many probabilistic object detectors have been proposed. However, there is no summary on uncertainty estimation in deep object detection, and existing methods are not only built with different network architectures and uncertainty estimation methods, but also evaluated on different datasets with a wide range of evaluation metrics. As a result, a comparison among methods remains challenging, as does the selection of a model that best suits a particular application. This paper aims to alleviate this problem by providing a review and comparative study on existing probabilistic object detection methods for autonomous driving applications. First, we provide an overview of generic uncertainty estimation in deep learning, and then systematically survey existing methods and evaluation metrics for probabilistic object detection. Next, we present a strict comparative study for probabilistic object detection based on an image detector and three public autonomous driving datasets. Finally, we present a discussion of the remaining challenges and future works. Code has been made available at https://github.com/asharakeh/pod_compare.git

Related papers

• Bayesian Detector Combination for Object Detection with Crowdsourced Annotations [49.43709660948812]
  Acquiring fine-grained object detection annotations in unconstrained images is time-consuming, expensive, and prone to noise. We propose a novel Bayesian Detector Combination (BDC) framework to more effectively train object detectors with noisy crowdsourced annotations. BDC is model-agnostic, requires no prior knowledge of the annotators' skill level, and seamlessly integrates with existing object detection models.
  arXiv  Detail & Related papers  (2024-07-10T18:00:54Z)
• Uncertainty-Aware AB3DMOT by Variational 3D Object Detection [74.8441634948334]
  Uncertainty estimation is an effective tool to provide statistically accurate predictions. In this paper, we propose a Variational Neural Network-based TANet 3D object detector to generate 3D object detections with uncertainty.
  arXiv  Detail & Related papers  (2023-02-12T14:30:03Z)
• Object Detection as Probabilistic Set Prediction [3.7599363231894176]
  We present a proper scoring rule for evaluating and training probabilistic object detectors. Our results indicate that the training of existing detectors is optimized toward non-probabilistic metrics.
  arXiv  Detail & Related papers  (2022-03-15T15:13:52Z)
• The Familiarity Hypothesis: Explaining the Behavior of Deep Open Set Methods [86.39044549664189]
  Anomaly detection algorithms for feature-vector data identify anomalies as outliers, but outlier detection has not worked well in deep learning. This paper proposes the Familiarity Hypothesis that these methods succeed because they are detecting the absence of familiar learned features rather than the presence of novelty. The paper concludes with a discussion of whether familiarity detection is an inevitable consequence of representation learning.
  arXiv  Detail & Related papers  (2022-03-04T18:32:58Z)
• Probabilistic Approach for Road-Users Detection [0.0]
  One of the major challenges in state-of-the-art deep-learning based object detection are false positives which occur with overconfident scores. This paper proposes an approach to alleviate the problem of overconfident predictions by introducing a novel probabilistic layer to deep object detection networks in testing. It is demonstrated that the proposed technique reduces overconfidence in the false positives without degrading the performance on the true positives.
  arXiv  Detail & Related papers  (2021-12-02T16:02:08Z)
• Uncertainty Aware Proposal Segmentation for Unknown Object Detection [13.249453757295083]
  This paper proposes to exploit additional predictions of semantic segmentation models and quantifying its confidences. We use object proposals generated by Region Proposal Network (RPN) and adapt distance aware uncertainty estimation of semantic segmentation. The augmented object proposals are then used to train a classifier for known vs. unknown objects categories.
  arXiv  Detail & Related papers  (2021-11-25T01:53:05Z)
• CertainNet: Sampling-free Uncertainty Estimation for Object Detection [65.28989536741658]
  Estimating the uncertainty of a neural network plays a fundamental role in safety-critical settings. In this work, we propose a novel sampling-free uncertainty estimation method for object detection. We call it CertainNet, and it is the first to provide separate uncertainties for each output signal: objectness, class, location and size.
  arXiv  Detail & Related papers  (2021-10-04T17:59:31Z)
• Labels Are Not Perfect: Inferring Spatial Uncertainty in Object Detection [26.008419879970365]
  In this work, we infer the uncertainty in bounding box labels from LiDAR point clouds based on a generative model. Comprehensive experiments show that the proposed model reflects complex environmental noises in LiDAR perception and the label quality. We propose Jaccard IoU as a new evaluation metric that extends IoU by incorporating label uncertainty.
  arXiv  Detail & Related papers  (2020-12-18T09:11:44Z)
• 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)
• Inferring Spatial Uncertainty in Object Detection [35.28872968233385]
  We propose a generative model to estimate bounding box label uncertainties from LiDAR point clouds. Comprehensive experiments show that the proposed model represents uncertainties commonly seen in driving scenarios. We propose an extension of IoU, called the Jaccard IoU (JIoU), as a new evaluation metric that incorporates label uncertainty.
  arXiv  Detail & Related papers  (2020-03-07T19:29:43Z)
• CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus [62.86856923633923]
  We present a robust estimator for fitting multiple parametric models of the same form to noisy measurements. In contrast to previous works, which resorted to hand-crafted search strategies for multiple model detection, we learn the search strategy from data. For self-supervised learning of the search, we evaluate the proposed algorithm on multi-homography estimation and demonstrate an accuracy that is superior to state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-01-08T17:37:01Z)

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.