PoseDriver: A Unified Approach to Multi-Category Skeleton Detection for Autonomous Driving

• URL: http://arxiv.org/abs/2603.23215v2
• Date: Wed, 25 Mar 2026 09:59:01 GMT
• Title: PoseDriver: A Unified Approach to Multi-Category Skeleton Detection for Autonomous Driving
• Abstract summary: PoseDriver is a unified framework for bottom-up multi-category skeleton detection tailored to common objects in driving scenarios.<n>We propose a novel approach for lane detection based on skeleton representations, achieving state-of-the-art performance on the OpenLane dataset.<n>We present a new dataset for bicycle skeleton detection and assess the transferability of our framework to novel categories.
• Score: 72.70946892443236
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Object skeletons offer a concise representation of structural information, capturing essential aspects of posture and orientation that are crucial for autonomous driving applications. However, a unified architecture that simultaneously handles multiple instances and categories using only the input image remains elusive. In this paper, we introduce PoseDriver, a unified framework for bottom-up multi-category skeleton detection tailored to common objects in driving scenarios. We model each category as a distinct task to systematically address the challenges of multi-task learning. Specifically, we propose a novel approach for lane detection based on skeleton representations, achieving state-of-the-art performance on the OpenLane dataset. Moreover, we present a new dataset for bicycle skeleton detection and assess the transferability of our framework to novel categories. Experimental results validate the effectiveness of the proposed approach.

Related papers

• Multimodal Scenario Similarity Search for Autonomous Driving [0.0]
  Large-scale autonomous-driving datasets contain vast numbers of recorded scenarios.<n>Existing approaches rely on either visual representations or motion-based descriptions.<n>We present a framework for autonomous-driving scenario retrieval that combines visual and trajectory-based representations.
  arXiv  Detail & Related papers  (2026-07-10T13:55:55Z)
• Appearance-Based Refinement for Object-Centric Motion Segmentation [85.2426540999329]
  We introduce an appearance-based refinement method that leverages temporal consistency in video streams to correct inaccurate flow-based proposals. Our approach involves a sequence-level selection mechanism that identifies accurate flow-predicted masks as exemplars. Its performance is evaluated on multiple video segmentation benchmarks, including DAVIS, YouTube, SegTrackv2, and FBMS-59.
  arXiv  Detail & Related papers  (2023-12-18T18:59:51Z)
• Labeling Indoor Scenes with Fusion of Out-of-the-Box Perception Models [4.157013247909771]
  We propose to leverage the recent advancements in state-of-the-art models for bottom-up segmentation (SAM), object detection (Detic), and semantic segmentation (MaskFormer) We aim to develop a cost-effective labeling approach to obtain pseudo-labels for semantic segmentation and object instance detection in indoor environments. We demonstrate the effectiveness of the proposed approach on the Active Vision dataset and the ADE20K dataset.
  arXiv  Detail & Related papers  (2023-11-17T21:58:26Z)
• Multi-grained Temporal Prototype Learning for Few-shot Video Object Segmentation [156.4142424784322]
  Few-Shot Video Object (FSVOS) aims to segment objects in a query video with the same category defined by a few annotated support images. We propose to leverage multi-grained temporal guidance information for handling the temporal correlation nature of video data. Our proposed video IPMT model significantly outperforms previous models on two benchmark datasets.
  arXiv  Detail & Related papers  (2023-09-20T09:16:34Z)
• LVLane: Deep Learning for Lane Detection and Classification in Challenging Conditions [2.5641096293146712]
  We present an end-to-end lane detection and classification system based on deep learning methodologies. In our study, we introduce a unique dataset meticulously curated to encompass scenarios that pose significant challenges for state-of-the-art (SOTA) lane localization models. We propose a CNN-based classification branch, seamlessly integrated with the detector, facilitating the identification of distinct lane types.
  arXiv  Detail & Related papers  (2023-07-13T16:09:53Z)
• Uncertainty Aware Active Learning for Reconfiguration of Pre-trained Deep Object-Detection Networks for New Target Domains [0.0]
  Object detection is one of the most important and fundamental aspects of computer vision tasks. To obtain training data for object detection model efficiently, many datasets opt to obtain their unannotated data in video format. Annotating every frame from a video is costly and inefficient since many frames contain very similar information for the model to learn from. In this paper, we proposed a novel active learning algorithm for object detection models to tackle this problem.
  arXiv  Detail & Related papers  (2023-03-22T17:14:10Z)
• Ensemble Learning for Fusion of Multiview Vision with Occlusion and Missing Information: Framework and Evaluations with Real-World Data and Applications in Driver Hand Activity Recognition [0.0]
  Multi-sensor frameworks provide opportunities for ensemble learning and sensor fusion. We propose and analyze an imputation scheme to handle missing information. We show that a late-fusion approach between parallel convolutional neural networks can outperform even the best-placed single camera model.
  arXiv  Detail & Related papers  (2023-01-30T00:24:27Z)
• Learning from Temporal Spatial Cubism for Cross-Dataset Skeleton-based Action Recognition [88.34182299496074]
  Action labels are only available on a source dataset, but unavailable on a target dataset in the training stage. We utilize a self-supervision scheme to reduce the domain shift between two skeleton-based action datasets. By segmenting and permuting temporal segments or human body parts, we design two self-supervised learning classification tasks.
  arXiv  Detail & Related papers  (2022-07-17T07:05:39Z)
• Representation-Centric Survey of Skeletal Action Recognition and the ANUBIS Benchmark [43.00059447663327]
  3D skeleton-based human action recognition has emerged as a powerful alternative to traditional RGB and depth-based approaches.<n>Despite remarkable progress, current research remains fragmented across diverse input representations.<n>ANUBIS is a large-scale, challenging skeleton action dataset designed to address critical gaps in existing benchmarks.
  arXiv  Detail & Related papers  (2022-05-04T14:03:43Z)
• Distribution Alignment: A Unified Framework for Long-tail Visual Recognition [52.36728157779307]
  We propose a unified distribution alignment strategy for long-tail visual recognition. We then introduce a generalized re-weight method in the two-stage learning to balance the class prior. Our approach achieves the state-of-the-art results across all four recognition tasks with a simple and unified framework.
  arXiv  Detail & Related papers  (2021-03-30T14:09:53Z)
• Adaptive Object Detection with Dual Multi-Label Prediction [78.69064917947624]
  We propose a novel end-to-end unsupervised deep domain adaptation model for adaptive object detection. The model exploits multi-label prediction to reveal the object category information in each image. We introduce a prediction consistency regularization mechanism to assist object detection.
  arXiv  Detail & Related papers  (2020-03-29T04:23:22Z)
• Contextual Encoder-Decoder Network for Visual Saliency Prediction [42.047816176307066]
  We propose an approach based on a convolutional neural network pre-trained on a large-scale image classification task. We combine the resulting representations with global scene information for accurately predicting visual saliency. Compared to state of the art approaches, the network is based on a lightweight image classification backbone.
  arXiv  Detail & Related papers  (2019-02-18T16:15:25Z)

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.