Papers Traffic Object Detection
“Traffic Object Detection” 태그가 달린 논문 20편 · 필터 해제
How Real is CARLAs Dynamic Vision Sensor? A Study on the Sim-to-Real Gap in Traffic Object Detection
Event cameras are gaining traction in traffic monitoring applications due to their low latency, high temporal resolution, and energy efficiency, which makes them well-suited for real-time object detection at traffic inte…
Domain Adaptationobject-detectionObject DetectionReal-Time Object Detection+1EMF: Event Meta Formers for Event-based Real-time Traffic Object Detection
Event cameras have higher temporal resolution, and require less storage and bandwidth compared to traditional RGB cameras. However, due to relatively lagging performance of event-based approaches, event cameras have not …
Autonomous DrivingObjectobject-detectionObject Detection+2TriLiteNet: Lightweight Model for Multi-Task Visual Perception
Efficient perception models are essential for Advanced Driver Assistance Systems (ADAS), as these applications require rapid processing and response to ensure safety and effectiveness in real-world environments. To addre…
Autonomous DrivingComputational EfficiencyDrivable Area DetectionLane Detection+4TUMTraffic-VideoQA: A Benchmark for Unified Spatio-Temporal Video Understanding in Traffic Scenes
We present TUMTraffic-VideoQA, a novel dataset and benchmark designed for spatio-temporal video understanding in complex roadside traffic scenarios. The dataset comprises 1,000 videos, featuring 85,000 multiple-choice QA…
Autonomous DrivingMultiple-choiceObjectQuestion Answering+3HeightFormer: A Semantic Alignment Monocular 3D Object Detection Method from Roadside Perspective
The on-board 3D object detection technology has received extensive attention as a critical technology for autonomous driving, while few studies have focused on applying roadside sensors in 3D traffic object detection. Ex…
3D Object DetectionAutonomous DrivingMonocular 3D Object DetectionObject+3Performance Evaluation of Real-Time Object Detection for Electric Scooters
Electric scooters (e-scooters) have rapidly emerged as a popular mode of transportation in urban areas, yet they pose significant safety challenges. In the United States, the rise of e-scooters has been marked by a conce…
Autonomous VehiclesBenchmarkingObjectobject-detection+3Real-time Traffic Object Detection for Autonomous Driving
With recent advances in computer vision, it appears that autonomous driving will be part of modern society sooner rather than later. However, there are still a significant number of concerns to address. Although modern c…
Autonomous DrivingObjectobject-detectionObject Detection+2RainSD: Rain Style Diversification Module for Image Synthesis Enhancement using Feature-Level Style Distribution
Autonomous driving technology nowadays targets to level 4 or beyond, but the researchers are faced with some limitations for developing reliable driving algorithms in diverse challenges. To promote the autonomous vehicle…
Autonomous DrivingAutonomous VehiclesDataset GenerationImage Generation+8FusionViT: Hierarchical 3D Object Detection via LiDAR-Camera Vision Transformer Fusion
For 3D object detection, both camera and lidar have been demonstrated to be useful sensory devices for providing complementary information about the same scenery with data representations in different modalities, e.g., 2…
3D Object DetectionObjectobject-detectionObject Detection+2Enhancing Traffic Object Detection in Variable Illumination with RGB-Event Fusion
Traffic object detection under variable illumination is challenging due to the information loss caused by the limited dynamic range of conventional frame-based cameras. To address this issue, we introduce bio-inspired ev…
Objectobject-detectionObject DetectionTraffic Object DetectionYou Only Look at Once for Real-time and Generic Multi-Task
High precision, lightweight, and real-time responsiveness are three essential requirements for implementing autonomous driving. In this study, we incorporate A-YOLOM, an adaptive, real-time, and lightweight multi-task mo…
Autonomous DrivingDrivable Area DetectionLane Detectionobject-detection+3Rethinking the Detection Head Configuration for Traffic Object Detection
Multi-scale detection plays an important role in object detection models. However, researchers usually feel blank on how to reasonably configure detection heads combining multi-scale features at different input resolutio…
Objectobject-detectionObject DetectionTraffic Object DetectionEffective Adaptation in Multi-Task Co-Training for Unified Autonomous Driving
Aiming towards a holistic understanding of multiple downstream tasks simultaneously, there is a need for extracting features with better transferability. Though many latest self-supervised pre-training methods have achie…
Autonomous DrivingMulti-Task Learningobject-detectionObject Detection+2Efficient Perception, Planning, and Control Algorithm for Vision-Based Automated Vehicles
Autonomous vehicles have limited computational resources and thus require efficient control systems. The cost and size of sensors have limited the development of self-driving cars. To overcome these restrictions, this st…
Autonomous DrivingAutonomous VehiclesMotion Planningobject-detection+3YOLOPv2: Better, Faster, Stronger for Panoptic Driving Perception
Over the last decade, multi-tasking learning approaches have achieved promising results in solving panoptic driving perception problems, providing both high-precision and high-efficiency performance. It has become a popu…
Autonomous DrivingDrivable Area DetectionLane DetectionMulti-Task Learning+3HybridNets: End-to-End Perception Network
End-to-end Network has become increasingly important in multi-tasking. One prominent example of this is the growing significance of a driving perception system in autonomous driving. This paper systematically studies an …
Autonomous DrivingDrivable Area DetectionLane Detectionobject-detection+2YOLOP: You Only Look Once for Panoptic Driving Perception
A panoptic driving perception system is an essential part of autonomous driving. A high-precision and real-time perception system can assist the vehicle in making the reasonable decision while driving. We present a panop…
Autonomous DrivingDrivable Area DetectionLane DetectionMulti-Task Learning+3You Only Learn One Representation: Unified Network for Multiple Tasks
People ``understand'' the world via vision, hearing, tactile, and also the past experience. Human experience can be learned through normal learning (we call it explicit knowledge), or subconsciously (we call it implicit …
Multi-Task LearningObject DetectionReal-Time Object DetectionTraffic Object DetectionFII-CenterNet: An Anchor-Free Detector With Foreground Attention for Traffic Object Detection
Most successful object detectors are anchor-based, which is difficult to adapt to the diversity of traffic objects. In this paper, we propose a novel anchor-free method, called FII-CenterNet, which introduces the foregro…
Diversityobject-detectionObject DetectionRegion Proposal+2CAIL2019-SCM: A Dataset of Similar Case Matching in Legal Domain
In this paper, we introduce CAIL2019-SCM, Chinese AI and Law 2019 Similar Case Matching dataset. CAIL2019-SCM contains 8,964 triplets of cases published by the Supreme People's Court of China. CAIL2019-SCM focuses on det…
Traffic Object Detection