Multi-cue onboard pedestrian detection

By Christian Wojek, Stefan Walk, Bernt Schiele. 2009

440 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

End-to-End People Detection in Crowded Scenes (2015, methodology): “the TUD-Brussels dataset [26] as the TUD-Crossing dataset does not provide a corresponding training set 4.”
nuScenes: A Multimodal Dataset for Autonomous Driving (2019, background): “Other datasets focus exclusively on pedestrian annotations on images [20, 25, 79, 24, 88, 23, 58].”
A discriminative deep model for pedestrian detection with occlusion handling (2012, methodology): “This dataset contains 1063 images from the datasets of Caltech, ETHZ, TUD-Brussels, INRIA, Caviar and our recorded images from surveillance cameras.”
Scalable Person Re-identification: A Benchmark (2015, methodology): “For other datasets, the codebook is trained on the independent TUD-Brussels dataset [35].”
EuroCity Persons: A Novel Benchmark for Person Detection in Traffic Scenes (2018, background): “Daimler-DB [11], INRIA [19], ETH [20], and TUDBrussels [21]).”
Single-Pedestrian Detection Aided by Multi-pedestrian Detection (2013, methodology): “The proposed framework is evaluated on three public datasets: Caltech [10], TUD-Brussels [36] and ETH [12].”
A Survey of Deep Learning-Based Object Detection (2019, neutral): “TUD-Brussels[57] mobile 1776 218 1092 1498 508”
Patch to the Future: Unsupervised Visual Prediction (2014, methodology): “For this experiment, since the (a) Original Image (b) Prediction Heatmap”
Pedestrian detection with a Large-Field-Of-View deep network (2015, background): “Pedestrian detection has been a topic of many recent works, many of which have had signiﬁcant impact [22], [5], [23], [6], [24], [7], [8], [25], [9].”
D2-City: A Large-Scale Dashcam Video Dataset of Diverse Traffic Scenarios (2019, background): “Traffic datasets in earlier stages were mainly for pedestrian detection and tracking [6, 7, 8, 19].”
Learning scene-specific pedestrian detectors without real data (2015, result): “This stands in contrast to the large body of work focused primarily on pedestrian detection from a mobile platform [12, 13, 41, 11].”
New features and insights for pedestrian detection (2010, methodology): “A similar observation was made by [27], who found that adding Haar features can sometimes help, but careful normalization is required, if the imaging conditions vary.”
Effective Fusion Factor in FPN for Tiny Object Detection (2020, background): “[4, 8, 31, 7, 6, 10, 34] are scene-rich and well-annotated datasets used for pedestrian detection task.”
CityPersons: A Diverse Dataset for Pedestrian Detection (2017, background): “INRIA [7], ETH [11], TudBrussels [29], and Daimler [10] represent early efforts to collect pedestrian datasets.”
Fast Feature Pyramids for Object Detection (2014, methodology): “We show results for both pedestrian detection (measured on the Caltech [31], INRIA [21], TUD- Brussels [36] and ETH [37] data sets) and general object detection (measured on the PASCAL VOC [38]).”
CrowdHuman: A Benchmark for Detecting Human in a Crowd (2018, background): “ection, COCOPerson for person detection, and Brainwash for head detection. 2. Related Work 2.1. Human detection datasets. Pioneer works of pedestrian detection datasets involve INRIA [3], TudBrussels [27], and Daimler [7]. These datasets have contributed to spurring interest and progress of human de”
Real-Time Pedestrian Detection with Deep Network Cascades (2015, methodology): “Pedestrian detection methods have employed a variety of techniques and features [4, 5, 11, 12, 33, 38, 39, 42].”
Making Bertha See (2013, neutral): “a variant of [27].”