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Found 17 papers, 9 papers with code
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.
Road Detection via On--line Label Transfer
The major challenges of road detection are dealing with shadows and lighting variations and the presence of other objects in the scene.
GPU-based Pedestrian Detection for Autonomous Driving
We propose a real-time pedestrian detection system for the embedded Nvidia Tegra X1 GPU-CPU hybrid platform.
A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images
Colorectal cancer (CRC) is the third cause of cancer death worldwide.
Slanted Stixels: Representing San Francisco's Steepest Streets
In this work we present a novel compact scene representation based on Stixels that infers geometric and semantic information.
Training a Binary Weight Object Detector by Knowledge Transfer for Autonomous Driving
Autonomous driving has harsh requirements of small model size and energy efficiency, in order to enable the embedded system to achieve real-time on-board object detection.
Is the Pedestrian going to Cross? Answering by 2D Pose Estimation
Our recent work suggests that, thanks to nowadays powerful CNNs, image-based 2D pose estimation is a promising cue for determining pedestrian intentions such as crossing the road in the path of the ego-vehicle, stopping before entering the road, and starting to walk or bending towards the road.
On Offline Evaluation of Vision-based Driving Models
We show that the correlation of offline evaluation with driving quality can be significantly improved by selecting an appropriate validation dataset and suitable offline metrics.
Exploring the Limitations of Behavior Cloning for Autonomous Driving
Driving requires reacting to a wide variety of complex environment conditions and agent behaviors.
Ranked #17 on
Autonomous Driving
on CARLA Leaderboard
Multimodal End-to-End Autonomous Driving
On the other hand, we find end-to-end driving approaches that try to learn a direct mapping from input raw sensor data to vehicle control signals.
Intention Recognition of Pedestrians and Cyclists by 2D Pose Estimation
In this paper, we show how the same methodology can be used for recognizing pedestrians and cyclists' intentions.
Active Learning for Deep Detection Neural Networks
In this paper, we propose a method to perform active learning of object detectors based on convolutional neural networks.
Monocular Depth Estimation through Virtual-world Supervision and Real-world SfM Self-Supervision
In this paper, we perform monocular depth estimation by virtual-world supervision (MonoDEVS) and real-world SfM self-supervision.
Co-training for Deep Object Detection: Comparing Single-modal and Multi-modal Approaches
This data labeling bottleneck may be intensified due to domain shifts among image sensors, which could force per-sensor data labeling.
Co-Training for Unsupervised Domain Adaptation of Semantic Segmentation Models
In this paper, we propose a new co-training procedure for synth-to-real UDA of semantic segmentation models.
CARLA-BSP: a simulated dataset with pedestrians
We present a sample dataset featuring pedestrians generated using the ARCANE framework, a new framework for generating datasets in CARLA (0. 9. 13).
All for One, and One for All: UrbanSyn Dataset, the third Musketeer of Synthetic Driving Scenes
We introduce UrbanSyn, a photorealistic dataset acquired through semi-procedurally generated synthetic urban driving scenarios.
