Search Results for author:
Found 26 papers, 4 papers with code
Near Field iToF LIDAR Depth Improvement from Limited Number of Shots
Indirect Time of Flight LiDARs can indirectly calculate the scene's depth from the phase shift angle between transmitted and received laser signals with amplitudes modulated at a predefined frequency.
Revisiting Modality Imbalance In Multimodal Pedestrian Detection
Multimodal learning, particularly for pedestrian detection, has recently received emphasis due to its capability to function equally well in several critical autonomous driving scenarios such as low-light, night-time, and adverse weather conditions.
Fast and Efficient Scene Categorization for Autonomous Driving using VAEs
We train a Variational Autoencoder in an unsupervised manner and map images to a constrained multi-dimensional latent space and use the latent vectors as compact embeddings that serve as global descriptors for images.
Woodscape Fisheye Object Detection for Autonomous Driving -- CVPR 2022 OmniCV Workshop Challenge
Object detection is a comprehensively studied problem in autonomous driving.
Deep Multi-Task Networks For Occluded Pedestrian Pose Estimation
The proposed framework has improved state-of-the-art performances of pose estimation, pedestrian detection, and instance segmentation.
Ranked #20 on
Pose Estimation
on COCO test-dev
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.
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.
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.
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.
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.
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.
Dynamic Task Weighting Methods for Multi-task Networks in Autonomous Driving Systems
Deep multi-task networks are of particular interest for autonomous driving systems.
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.
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.
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.
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.
SoilingNet: Soiling Detection on Automotive Surround-View Cameras
Cameras are an essential part of sensor suite in autonomous driving.
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.
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.
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.
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.
