Search Results for author:
Found 70 papers, 8 papers with code
X$^3$KD: Knowledge Distillation Across Modalities, Tasks and Stages for Multi-Camera 3D Object Detection
Specifically, we propose cross-task distillation from an instance segmentation teacher (X-IS) in the PV feature extraction stage providing supervision without ambiguous error backpropagation through the view transformation.
Optical Flow for Autonomous Driving: Applications, Challenges and Improvements
Optical flow estimation is a well-studied topic for automated driving applications.
X-Align: Cross-Modal Cross-View Alignment for Bird's-Eye-View Segmentation
Bird's-eye-view (BEV) grid is a common representation for the perception of road components, e. g., drivable area, in autonomous driving.
Woodscape Fisheye Object Detection for Autonomous Driving -- CVPR 2022 OmniCV Workshop Challenge
Object detection is a comprehensively studied problem in autonomous driving.
Self-Supervised 3D Monocular Object Detection by Recycling Bounding Boxes
We evaluate the pretext task using the RTM3D detection model as baseline, with and without the application of data augmentation.
SpikiLi: A Spiking Simulation of LiDAR based Real-time Object Detection for Autonomous Driving
Spiking Neural Networks are a recent and new neural network design approach that promises tremendous improvements in power efficiency, computation efficiency, and processing latency.
ViT-BEVSeg: A Hierarchical Transformer Network for Monocular Birds-Eye-View Segmentation
In this paper, we evaluate the use of vision transformers (ViT) as a backbone architecture to generate BEV maps.
Surround-view Fisheye Camera Perception for Automated Driving: Overview, Survey and Challenges
Surround-view fisheye cameras are commonly used for near-field sensing in automated driving.
Neuroevolutionary Multi-objective approaches to Trajectory Prediction in Autonomous Vehicles
The incentive for using Evolutionary Algorithms (EAs) for the automated optimization and training of deep neural networks (DNNs), a process referred to as neuroevolution, has gained momentum in recent years.
UnShadowNet: Illumination Critic Guided Contrastive Learning For Shadow Removal
Shadows are frequently encountered natural phenomena that significantly hinder the performance of computer vision perception systems in practical settings, e. g., autonomous driving.
SynWoodScape: Synthetic Surround-view Fisheye Camera Dataset for Autonomous Driving
In this work, we release a synthetic version of the surround-view dataset, covering many of its weaknesses and extending it.
Detecting Adversarial Perturbations in Multi-Task Perception
In this paper, we (i) propose a novel adversarial perturbation detection scheme based on multi-task perception of complex vision tasks (i. e., depth estimation and semantic segmentation).
LiMoSeg: Real-time Bird's Eye View based LiDAR Motion Segmentation
To the best of our knowledge, this is the first work directly performing motion segmentation in LiDAR BEV space.
A Hybrid Sparse-Dense Monocular SLAM System for Autonomous Driving
In this paper, we present a system for incrementally reconstructing a dense 3D model of the geometry of an outdoor environment using a single monocular camera attached to a moving vehicle.
Woodscape Fisheye Semantic Segmentation for Autonomous Driving -- CVPR 2021 OmniCV Workshop Challenge
This challenge served as a medium to investigate the challenges and new methodologies to handle the complexities with perception on fisheye images.
Adversarial Attacks on Multi-task Visual Perception for Autonomous Driving
On the other hand, current deep neural networks are easily fooled by adversarial attacks.
BEV-MODNet: Monocular Camera based Bird's Eye View Moving Object Detection for Autonomous Driving
In this work, we explore end-to-end Moving Object Detection (MOD) on the BEV map directly using monocular images as input.
An Online Learning System for Wireless Charging Alignment using Surround-view Fisheye Cameras
In this work, we propose a system based on the surround-view camera architecture to detect, localize, and automatically align the vehicle with the inductive chargepad.
Spatio-Contextual Deep Network Based Multimodal Pedestrian Detection For Autonomous Driving
Fusion of these two encoded features takes place inside a multimodal feature embedding module (MuFEm) consisting of several groups of a pair of Graph Attention Network and a feature fusion unit.
Ensemble-based Semi-supervised Learning to Improve Noisy Soiling Annotations in Autonomous Driving
Manual annotation of soiling on surround view cameras is a very challenging and expensive task.
Weather and Light Level Classification for Autonomous Driving: Dataset, Baseline and Active Learning
There is no public dataset for weather and light level classification focused on autonomous driving to the best of our knowledge.
Computer vision in automated parking systems: Design, implementation and challenges
In this paper, we discuss the design and implementation of an automated parking system from the perspective of computer vision algorithms.
Vision-based Driver Assistance Systems: Survey, Taxonomy and Advances
Vision-based driver assistance systems is one of the rapidly growing research areas of ITS, due to various factors such as the increased level of safety requirements in automotive, computational power in embedded systems, and desire to get closer to autonomous driving.
VM-MODNet: Vehicle Motion aware Moving Object Detection for Autonomous Driving
In this work, we aim to leverage the vehicle motion information and feed it into the model to have an adaptation mechanism based on ego-motion.
BEVDetNet: Bird's Eye View LiDAR Point Cloud based Real-time 3D Object Detection for Autonomous Driving
Most of the research is focused on achieving higher accuracy and these models are not optimized for deployment on embedded systems from the perspective of latency and power efficiency.
Exploring 2D Data Augmentation for 3D Monocular Object Detection
Extension of these data augmentations for 3D object detection requires adaptation of the 3D geometry of the input scene and synthesis of new viewpoints.
SVDistNet: Self-Supervised Near-Field Distance Estimation on Surround View Fisheye Cameras
We evaluate our approach on the Fisheye WoodScape surround-view dataset, significantly improving over previous approaches.
Near-field Perception for Low-Speed Vehicle Automation using Surround-view Fisheye Cameras
In this work, we provide a detailed survey of such vision systems, setting up the survey in the context of an architecture that can be decomposed into four modular components namely Recognition, Reconstruction, Relocalization, and Reorganization.
FisheyeSuperPoint: Keypoint Detection and Description Network for Fisheye Images
Keypoint detection and description is a commonly used building block in computer vision systems particularly for robotics and autonomous driving.
OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving
We obtain the state-of-the-art results on KITTI for depth estimation and pose estimation tasks and competitive performance on the other tasks.
Generalized Object Detection on Fisheye Cameras for Autonomous Driving: Dataset, Representations and Baseline
It is the first detailed study on object detection on fisheye cameras for autonomous driving scenarios to the best of our knowledge.
Learning Panoptic Segmentation from Instance Contours
Instance contours along with semantic segmentation yield a boundary aware semantic segmentation of things.
Monocular Instance Motion Segmentation for Autonomous Driving: KITTI InstanceMotSeg Dataset and Multi-task Baseline
Moving object segmentation is a crucial task for autonomous vehicles as it can be used to segment objects in a class agnostic manner based on their motion cues.
SynDistNet: Self-Supervised Monocular Fisheye Camera Distance Estimation Synergized with Semantic Segmentation for Autonomous Driving
This paper introduces a novel multi-task learning strategy to improve self-supervised monocular distance estimation on fisheye and pinhole camera images.
Beyond Single Stage Encoder-Decoder Networks: Deep Decoders for Semantic Image Segmentation
In order to further improve the architecture we introduce a weight function which aims to re-balance classes to increase the attention of the networks to under-represented objects.
UnRectDepthNet: Self-Supervised Monocular Depth Estimation using a Generic Framework for Handling Common Camera Distortion Models
To further illustrate the general applicability of the proposed framework, we apply it to wide-angle fisheye cameras with 190$^\circ$ horizontal field of view.
TiledSoilingNet: Tile-level Soiling Detection on Automotive Surround-view Cameras Using Coverage Metric
Localized detection of soiling in an image is necessary to control the cleaning system.
Deep Reinforcement Learning for Autonomous Driving: A Survey
With the development of deep representation learning, the domain of reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies in high dimensional environments.
Dynamic Task Weighting Methods for Multi-task Networks in Autonomous Driving Systems
Deep multi-task networks are of particular interest for autonomous driving systems.
Trained Trajectory based Automated Parking System using Visual SLAM on Surround View Cameras
Existing parking systems build a local map to be able to plan for maneuvering towards a detected slot.
FisheyeMultiNet: Real-time Multi-task Learning Architecture for Surround-view Automated Parking System
We provide a holistic overview of an industrial system covering the embedded system, use cases and the deep learning architecture.
Let's Get Dirty: GAN Based Data Augmentation for Camera Lens Soiling Detection in Autonomous Driving
We propose a novel GAN based algorithm for generating unseen patterns of soiled images.
RST-MODNet: Real-time Spatio-temporal Moving Object Detection for Autonomous Driving
The trajectory of the ego-vehicle is planned based on the future states of detected moving objects.
FuseMODNet: Real-Time Camera and LiDAR based Moving Object Detection for robust low-light Autonomous Driving
In this work, we propose a robust and real-time CNN architecture for Moving Object Detection (MOD) under low-light conditions by capturing motion information from both camera and LiDAR sensors.
FisheyeDistanceNet: Self-Supervised Scale-Aware Distance Estimation using Monocular Fisheye Camera for Autonomous Driving
Fisheye cameras are commonly used in applications like autonomous driving and surveillance to provide a large field of view ($>180^{\circ}$).
FisheyeMODNet: Moving Object detection on Surround-view Cameras for Autonomous Driving
In this work, we propose a CNN architecture for moving object detection using fisheye images that were captured in autonomous driving environment.
RGB and LiDAR fusion based 3D Semantic Segmentation for Autonomous Driving
Motivated by the fact that semantic segmentation is a mature algorithm on image data, we explore sensor fusion based 3D segmentation.
SoilingNet: Soiling Detection on Automotive Surround-View Cameras
Cameras are an essential part of sensor suite in autonomous driving.
WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving
Fisheye cameras are commonly employed for obtaining a large field of view in surveillance, augmented reality and in particular automotive applications.
MultiNet++: Multi-Stream Feature Aggregation and Geometric Loss Strategy for Multi-Task Learning
In this work, we propose a multi-stream multi-task network to take advantage of using feature representations from preceding frames in a video sequence for joint learning of segmentation, depth, and motion.
Capsule Neural Network based Height Classification using Low-Cost Automotive Ultrasonic Sensors
High performance ultrasonic sensor hardware is mainly used in medical applications.
Realistic Ultrasonic Environment Simulation Using Conditional Generative Adversarial Networks
For the best of our knowledge, we present the first realistic data augmentation for automotive ultrasonics.
NeurAll: Towards a Unified Model for Visual Perception in Automated Driving
In this paper, we propose a joint multi-task network design for learning several tasks simultaneously.
Yes, we GAN: Applying Adversarial Techniques for Autonomous Driving
Generative Adversarial Networks (GAN) have gained a lot of popularity from their introduction in 2014 till present.
Challenges in Designing Datasets and Validation for Autonomous Driving
There is a large gap between academic and industrial setting and a substantial way from a research prototype, built on public datasets, to a deployable solution which is a challenging task.
Design of Real-time Semantic Segmentation Decoder for Automated Driving
Semantic segmentation remains a computationally intensive algorithm for embedded deployment even with the rapid growth of computation power.
AuxNet: Auxiliary tasks enhanced Semantic Segmentation for Automated Driving
Decision making in automated driving is highly specific to the environment and thus semantic segmentation plays a key role in recognizing the objects in the environment around the car.
Real-time Joint Object Detection and Semantic Segmentation Network for Automated Driving
In this paper, we present a joint multi-task network design for learning object detection and semantic segmentation simultaneously.
Exploring Deep Spiking Neural Networks for Automated Driving Applications
Neural networks have become the standard model for various computer vision tasks in automated driving including semantic segmentation, moving object detection, depth estimation, visual odometry, etc.
Optical Flow augmented Semantic Segmentation networks for Automated Driving
We also make use of the ground truth optical flow in Virtual KITTI to serve as an ideal estimator and a standard Farneback optical flow algorithm to study the effect of noise.
Multi-stream CNN based Video Semantic Segmentation for Automated Driving
We propose two simple high-level architectures based on Recurrent FCN (RFCN) and Multi-Stream FCN (MSFCN) networks.
Exploring applications of deep reinforcement learning for real-world autonomous driving systems
Deep Reinforcement Learning (DRL) has become increasingly powerful in recent years, with notable achievements such as Deepmind's AlphaGo.
Real-time Dynamic Object Detection for Autonomous Driving using Prior 3D-Maps
In this paper, we discuss real-time dynamic object detection algorithms which leverages previously mapped Lidar point clouds to reduce processing.
Monocular Fisheye Camera Depth Estimation Using Sparse LiDAR Supervision
For instance, they do not capture the extensive variability in the appearance of objects like vehicles present in real datasets.
RTSeg: Real-time Semantic Segmentation Comparative Study
In this paper, we address this gap by presenting a real-time semantic segmentation benchmarking framework with a decoupled design for feature extraction and decoding methods.
MODNet: Moving Object Detection Network with Motion and Appearance for Autonomous Driving
Our experiments show that the proposed method outperforms state of the art methods that utilize motion cue only with 21. 5% in mAP on KITTI MOD.
Deep Semantic Segmentation for Automated Driving: Taxonomy, Roadmap and Challenges
In this paper, the semantic segmentation problem is explored from the perspective of automated driving.
Rejection-Cascade of Gaussians: Real-time adaptive background subtraction framework
We achieve a good improvement in speed without compromising the accuracy with respect to the baseline GMM model.
Deep Reinforcement Learning framework for Autonomous Driving
This is of particular relevance as it is difficult to pose autonomous driving as a supervised learning problem due to strong interactions with the environment including other vehicles, pedestrians and roadworks.
End-to-End Deep Reinforcement Learning for Lane Keeping Assist
This is of particular interest as it is difficult to pose autonomous driving as a supervised learning problem as it has a strong interaction with the environment including other vehicles, pedestrians and roadworks.
