PPD: A New Valet Parking Pedestrian Fisheye Dataset for Autonomous Driving
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Abstract (arXiv)
Pedestrian detection under valet parking scenarios is fundamental for autonomous driving. However, the presence of pedestrians can be manifested in a variety of ways and postures under imperfect ambient conditions, which can adversely affect detection performance. Furthermore, models trained on publicdatasets that include pedestrians generally provide suboptimal outcomes for these valet parking scenarios. In this paper, wepresent the Parking Pedestrian Dataset (PPD), a large-scale fisheye dataset to support research dealing with real-world pedestrians, especially with occlusions and diverse postures. PPD consists of several distinctive types of pedestrians captured with fisheye cameras. Additionally, we present a pedestrian detection baseline on PPD dataset, and introduce two data augmentation techniques to improve the baseline by enhancing the diversity ofthe original dataset. Extensive experiments validate the effectiveness of our novel data augmentation approaches over baselinesand the dataset's exceptional generalizability.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 32 of its 56 references that are in the map. First posted on arXiv 20 Sep 2023.
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References · 32
- 2014Microsoft COCO: Common Objects in Contextcited by 17,608
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
- 2017Mask R-CNNcited by 7,277
- 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
- 2015SSD: Single Shot MultiBox Detectorcited by 3,130
- 2019CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Featurescited by 2,884
- 2017Improved Regularization of Convolutional Neural Networks with Cutoutcited by 1,628
- 2016Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learningcited by 1,512
- 2019CenterNet: Keypoint Triplets for Object Detectioncited by 654
- 2020Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentationcited by 596
- 2018Object Detection with Deep Learning: A Reviewcited by 528
- 2019Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learningcited by 510
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- 2017Deep Layer Aggregationcited by 436
- 2019Cascade R-CNN: High Quality Object Detection and Instance Segmentationcited by 429
- 2017Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detectioncited by 209
- 2017CityPersons: A Diverse Dataset for Pedestrian Detectioncited by 172
- 2019Liquid Warping GAN: A Unified Framework for Human Motion Imitation, Appearance Transfer and Novel View Synthesiscited by 161
- 2019WoodScape: A multi-task, multi-camera fisheye dataset for autonomous drivingcited by 144
- 2017DSSD : Deconvolutional Single Shot Detectorcited by 131
- 2017Single-Shot Refinement Neural Network for Object Detectioncited by 129
- 2021You Only Look One-level Featurecited by 116
- 2019Rethinking Classification and Localization for Object Detectioncited by 98
- 2019Adaptive NMS: Refining Pedestrian Detection in a Crowdcited by 66
- 2017Repulsion Loss: Detecting Pedestrians in a Crowdcited by 62
- 2019WiderPerson: A Diverse Dataset for Dense Pedestrian Detection in the Wildcited by 39
- 2015Scale-aware Fast R-CNN for Pedestrian Detectioncited by 32
- 2021Video action recognition for lane-change classification and prediction of surrounding vehiclescited by 14
- 2021Data Augmentation in a Hybrid Approach for Aspect-Based Sentiment Analysiscited by 10
- 2020PSDet: Efficient and Universal Parking Slot Detectioncited by 7
- 2018Real-time Detection, Tracking, and Classification of Moving and Stationary Objects using Multiple Fisheye Imagescited by 6
- 2021Disentangling and Vectorization: A 3D Visual Perception Approach for Autonomous Driving Based on Surround-View Fisheye Camerascited by 4
- 2021DeepWORD: A GCN-based Approach for Owner-Member Relationship Detection in Autonomous Drivingcited by 2
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