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The Cityscapes Dataset for Semantic Urban Scene Understanding
TLDR
We introduce Cityscapes, a benchmark suite and large-scale dataset to train and test approaches for pixel-level and instance-level semantic labeling. Expand
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Monocular Pedestrian Detection: Survey and Experiments
  • M. Enzweiler, D. Gavrila
  • Computer Science, Medicine
  • IEEE Transactions on Pattern Analysis and Machine…
  • 1 December 2009
TLDR
We evaluate a diverse set of state-of-the-art systems: wavelet-based AdaBoost cascade, HOG/linSVM, NN/LRF, and combined shape-texture detection. Expand
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Making Bertha Drive—An Autonomous Journey on a Historic Route
TLDR
We show that autonomous driving is feasible — not only on highways but even in very complex urban areas such as the Bertha Benz memorial route. Expand
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Multi-cue pedestrian classification with partial occlusion handling
TLDR
This paper presents a novel mixture-of-experts framework for pedestrian classification with partial occlusion handling. Expand
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The Cityscapes Dataset
TLDR
We present ongoing work on a new large-scale dataset for (1) assessing the performance of vision algorithms for different tasks of semantic urban scene understanding, including scene labeling, instance-level scene labeling and object detection. Expand
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A mixed generative-discriminative framework for pedestrian classification
  • M. Enzweiler, D. Gavrila
  • Mathematics, Computer Science
  • IEEE Conference on Computer Vision and Pattern…
  • 23 June 2008
TLDR
This paper presents a novel approach to pedestrian classification which involves utilizing the synthesized virtual samples of a learned generative model to enhance the classification performance of a discriminative model. Expand
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A Multilevel Mixture-of-Experts Framework for Pedestrian Classification
TLDR
We present a novel multilevel Mixture-of-Experts approach to combine information from multiple features and cues with the objective of improved pedestrian classification. Expand
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A new benchmark for stereo-based pedestrian detection
TLDR
We introduce the Daimler Stereo-Vision Pedestrian Detection benchmark, which consists of several thousands of pedestrians in the training set, and a 27-min test drive through urban environment and associated vehicle data. Expand
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Efficient Multi-cue Scene Segmentation
TLDR
This paper presents a novel multi-cue framework for scene segmentation, involving a combination of appearance (grayscale images) and depth cues (dense stereo vision). Expand
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Semantic Stixels: Depth is not enough
TLDR
We present Semantic Stixels, a novel vision-based scene model geared towards automated driving. Expand
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