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Found 40 papers, 19 papers with code
3D Multi-Object Tracking Employing MS-GLMB Filter for Autonomous Driving
However, both filters depend on overlapping fields of view from the cameras to combine complementary information.
NBBOX: Noisy Bounding Box Improves Remote Sensing Object Detection
Data augmentation has shown significant advancements in computer vision to improve model performance over the years, particularly in scenarios with limited and insufficient data.
Rethinking Feature Backbone Fine-tuning for Remote Sensing Object Detection
Fine-tuning the backbone is then typically required to generate features suitable for remote-sensing images.
Visual Multi-Object Tracking with Re-Identification and Occlusion Handling using Labeled Random Finite Sets
In occlusion handling, the filter's efficacy is dictated by trade-offs between the sophistication of the occlusion model and computational demand.
Track Initialization and Re-Identification for~3D Multi-View Multi-Object Tracking
Specifically, we exploit the 2D detections and extracted features from multiple cameras to provide a better approximation of the multi-object filtering density to realize the track initiation/termination and re-identification functionalities.
WaveDH: Wavelet Sub-bands Guided ConvNet for Efficient Image Dehazing
In this paper, we introduce WaveDH, a novel and compact ConvNet designed to address this efficiency gap in image dehazing.
Adaptive Confidence Threshold for ByteTrack in Multi-Object Tracking
ByteTrack, a simple tracking algorithm, enables the simultaneous tracking of multiple objects by strategically incorporating detections with a low confidence threshold.
Radar-Lidar Fusion for Object Detection by Designing Effective Convolution Networks
This underscores the value of radar-Lidar fusion in achieving precise object detection and localization, especially in challenging weather conditions.
Exploring Contextual Representation and Multi-Modality for End-to-End Autonomous Driving
Recent perception systems enhance spatial understanding with sensor fusion but often lack full environmental context.
3D Convolutional with Attention for Action Recognition
The current action recognition methods use computationally expensive models for learning spatio-temporal dependencies of the action.
Light Robust Monocular Depth Estimation For Outdoor Environment Via Monochrome And Color Camera Fusion
Our methods not only outperform the state-of-the-art works across all metrics but also efficient in terms of cost, memory, and computation.
Multi-Modal Fusion for Sensorimotor Coordination in Steering Angle Prediction
The conventional frame-based RGB camera is the most common exteroceptive sensor modality used to acquire the environmental perception data.
Resolving Camera Position for a Practical Application of Gaze Estimation on Edge Devices
In this paper, we carry out a study on gaze estimation with a logical camera setup position.
Server-Side Local Gradient Averaging and Learning Rate Acceleration for Scalable Split Learning
In recent years, there have been great advances in the field of decentralized learning with private data.
ARTSeg: Employing Attention for Thermal images Semantic Segmentation
Therefore, the neural network algorithms developed using these exteroceptive sensors have provided the necessary solution for the autonomous vehicle's perception.
Task-Driven Deep Image Enhancement Network for Autonomous Driving in Bad Weather
Visual perception in autonomous driving is a crucial part of a vehicle to navigate safely and sustainably in different traffic conditions.
Label-Attention Transformer with Geometrically Coherent Objects for Image Captioning
We propose label-attention Transformer with geometrically coherent objects (LATGeO).
RVMDE: Radar Validated Monocular Depth Estimation for Robotics
This work explores the utility of coarse signals from radar when fused with fine-grained data from a monocular camera for depth estimation in harsh environmental conditions.
SSTN: Self-Supervised Domain Adaptation Thermal Object Detection for Autonomous Driving
For this purpose, we have proposed a deep neural network Self Supervised Thermal Network (SSTN) for learning the feature embedding to maximize the information between visible and infrared spectrum domain by contrastive learning, and later employing these learned feature representation for the thermal object detection using multi-scale encoder-decoder transformer network.
Image Captioning using Multiple Transformers for Self-Attention Mechanism
Real-time image captioning, along with adequate precision, is the main challenge of this research field.
Anomalous Event Recognition in Videos Based on Joint Learningof Motion and Appearance with Multiple Ranking Measures
We propose a framework, Deep-network with Multiple Ranking Measures(DMRMs), which addresses context-dependency using a joint learning technique for motion and appearance features.
Ranked #10 on
Anomaly Detection In Surveillance Videos
on UCF-Crime
Channel Boosting Feature Ensemble for Radar-based Object Detection
The proposed method's efficacy is extensively evaluated using the COCO evaluation metric, and the best-proposed model surpasses its state-of-the-art counterpart method by $12. 55\%$ and $12. 48\%$ in both good and good-bad weather conditions.
LDNet: End-to-End Lane Marking Detection Approach Using a Dynamic Vision Sensor
Traditional lane detection methods incorporate handcrafted or deep learning-based features followed by postprocessing techniques for lane extraction using frame-based RGB cameras.
Ranked #1 on
Lane Detection
on DET
Imbalanced Image Classification with Complement Cross Entropy
Recently, deep learning models have achieved great success in computer vision applications, relying on large-scale class-balanced datasets.
Online Multi-Object Tracking and Segmentation with GMPHD Filter and Mask-based Affinity Fusion
One affinity, for position and motion, is computed by using the GMPHD filter, and the other affinity, for appearance is computed by using the responses from a single object tracker such as a kernalized correlation filter.
N 2 C : Neural Network Controller Design Using Behavioral Cloning
In the first part of this study, we explore the pipeline of parsing decision commands from the path tracking algorithm to the controller and proposed a neural network-based controller ($N^2C$) using behavioral cloning.
Exploring Thermal Images for Object Detection in Underexposure Regions for Autonomous Driving
A thermal camera captures an image using the heat difference emitted by objects in the infrared spectrum, and object detection in thermal images becomes effective for autonomous driving in challenging conditions.
Context-Aware Multi-Task Learning for Traffic Scene Recognition in Autonomous Vehicles
Specifically, we present a lower bound for the mutual information constraint between shared feature embedding and input that is considered to be able to extract common contextual information across tasks while preserving essential information of each task jointly.
Predictively Encoded Graph Convolutional Network for Noise-Robust Skeleton-based Action Recognition
In this work, we propose a skeleton-based action recognition method which is robust to noise information of given skeleton features.
Key Points Estimation and Point Instance Segmentation Approach for Lane Detection
In the case of traffic line detection, an essential perception module, many condition should be considered, such as number of traffic lines and computing power of the target system.
Ranked #42 on
Lane Detection
on CULane
(using extra training data)
3D ResNet with Ranking Loss Function for Abnormal Activity Detection in Videos
Afterwards, using these features and deep multiple instance learning along with the proposed ranking loss, our model learns to predict the abnormality score at the video segment level.
Ranked #13 on
Anomaly Detection In Surveillance Videos
on UCF-Crime
Unsupervised Pixel-level Road Defect Detection via Adversarial Image-to-Frequency Transform
To end this, we propose an unsupervised approach to detecting road defects, using Adversarial Image-to-Frequency Transform (AIFT).
Drivers Drowsiness Detection using Condition-Adaptive Representation Learning Framework
The proposed framework consists of four models: spatio-temporal representation learning, scene condition understanding, feature fusion, and drowsiness detection.
Practical License Plate Recognition in Unconstrained Surveillance Systems with Adversarial Super-Resolution
Although most current license plate (LP) recognition applications have been significantly advanced, they are still limited to ideal environments where training data are carefully annotated with constrained scenes.
Ranked #3 on
License Plate Recognition
on AOLP-RP
Unconstrained Road Marking Recognition with Generative Adversarial Networks
Recent road marking recognition has achieved great success in the past few years along with the rapid development of deep learning.
SNIDER: Single Noisy Image Denoising and Rectification for Improving License Plate Recognition
In this paper, we present an algorithm for real-world license plate recognition (LPR) from a low-quality image.
Ranked #1 on
License Plate Recognition
on AOLP-RP
Online Multi-Object Tracking Framework with the GMPHD Filter and Occlusion Group Management
In this paper, we propose an efficient online multi-object tracking framework based on the GMPHD filter and occlusion group management scheme where the GMPHD filter utilizes hierarchical data association to reduce the false negatives caused by miss detection.
Ranked #1 on
Online Multi-Object Tracking
on MOT15
Online Multiple Pedestrians Tracking using Deep Temporal Appearance Matching Association
The higher dimension of inherent information in the appearance model compared to the geometric model is problematic in many ways.
Multiple Hypothesis Tracking Algorithm for Multi-Target Multi-Camera Tracking with Disjoint Views
Furthermore, multi-target tracking within a camera is performed simultaneously with the tree formation by manipulating a status of each track hypothesis.
Online Multi-Object Tracking with Historical Appearance Matching and Scene Adaptive Detection Filtering
In this paper, we propose the methods to handle temporal errors during multi-object tracking.
