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Found 5 papers, 1 papers with code
Detecting Unexpected Obstacles for Self-Driving Cars: Fusing Deep Learning and Geometric Modeling
To utilize the appearance and contextual cues, we propose a new deep learning-based obstacle detection framework.
Lost and Found: Detecting Small Road Hazards for Self-Driving Vehicles
The proposed approach outperforms all considered baselines in our evaluations on both pixel and object level and runs at frame rates of up to 20 Hz on 2 mega-pixel stereo imagery.
The Cityscapes Dataset for Semantic Urban Scene Understanding
Visual understanding of complex urban street scenes is an enabling factor for a wide range of applications.
Hierarchical Adaptive Structural SVM for Domain Adaptation
In both cases, we show how HA-SSVM is effective in increasing the detection/recognition accuracy with respect to adaptation strategies that ignore the structure of the target data.
Spatiotemporal Stacked Sequential Learning for Pedestrian Detection
In particular, we propose to use two-stage classifiers which not only rely on the image descriptors required by the base classifiers but also on the response of such base classifiers in a given spatiotemporal neighborhood.
