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Found 31 papers, 11 papers with code
Evaluating the Effectiveness of Video Anomaly Detection in the Wild: Online Learning and Inference for Real-world Deployment
Video Anomaly Detection (VAD) identifies unusual activities in video streams, a key technology with broad applications ranging from surveillance to healthcare.
Enhancing Automatic Modulation Recognition for IoT Applications Using Transformers
Automatic modulation recognition (AMR) is vital for accurately identifying modulation types within incoming signals, a critical task for optimizing operations within edge devices in IoT ecosystems.
Understanding the Transit Gap: A Comparative Study of On-Demand Bus Services and Urban Climate Resilience in South End, Charlotte, NC and Avondale, Chattanooga, TN
Notably, doubling bus utilization decreased daily emissions by 10. 18% in South End and 8. 13% in Avondale, with a corresponding reduction in overall traffic.
Expert with Clustering: Hierarchical Online Preference Learning Framework
Even more so than the typical recommendation system, it is crucial to minimize regret, because 1) the mobility options directly affect the lives of the users, and 2) the system sustainability relies on sufficient user participation.
OCT-SelfNet: A Self-Supervised Framework with Multi-Modal Datasets for Generalized and Robust Retinal Disease Detection
Our method addresses the issue using a two-phase training approach that combines self-supervised pretraining and supervised fine-tuning with a mask autoencoder based on the SwinV2 backbone by providing a solution for real-world clinical deployment.
Integrating AI into CCTV Systems: A Comprehensive Evaluation of Smart Video Surveillance in Community Space
This article presents an AI-enabled Smart Video Surveillance (SVS) designed to enhance safety in community spaces such as educational and recreational areas, and small businesses.
VegaEdge: Edge AI Confluence Anomaly Detection for Real-Time Highway IoT-Applications
On top of that, we present VegaEdge - a sophisticated AI confluence designed for real-time security and surveillance applications in modern highway settings through edge-centric IoT-embedded platforms equipped with our anomaly detection approach.
VT-Former: An Exploratory Study on Vehicle Trajectory Prediction for Highway Surveillance through Graph Isomorphism and Transformer
This study seeks to explore both the advantages and the limitations inherent in combining transformer architecture with graphs for VTP.
Real-Time Online Unsupervised Domain Adaptation for Real-World Person Re-identification
This paper defines the setting of Real-world Real-time Online Unsupervised Domain Adaptation (R$^2$OUDA) for Person Re-identification.
Online unsupervised domain adaptation
Person Re-Identification
Real-Time Bus Arrival Prediction: A Deep Learning Approach for Enhanced Urban Mobility
A prevalent challenge is the mismatch between actual bus arrival times and their scheduled counterparts, leading to disruptions in fixed schedules.
Real-World Community-in-the-Loop Smart Video Surveillance -- A Case Study at a Community College
This paper presents a case study for designing and deploying smart video surveillance systems based on a real-world testbed at a community college.
A POV-based Highway Vehicle Trajectory Dataset and Prediction Architecture
We introduce the \emph{Carolinas Highway Dataset (CHD\footnote{\emph{CHD} available at: \url{https://github. com/TeCSAR-UNCC/Carolinas\_Dataset}})}, a vehicle trajectory, detection, and tracking dataset.
Understanding the Challenges and Opportunities of Pose-based Anomaly Detection
In this work, we analyze and quantify the characteristics of two well-known video anomaly datasets to better understand the difficulties of pose-based anomaly detection.
Understanding Policy and Technical Aspects of AI-Enabled Smart Video Surveillance to Address Public Safety
Finally, we propose quantitative and qualitative metrics to evaluate intelligent video surveillance systems.
Ancilia: Scalable Intelligent Video Surveillance for the Artificial Intelligence of Things
With the advancement of vision-based artificial intelligence, the proliferation of the Internet of Things connected cameras, and the increasing societal need for rapid and equitable security, the demand for accurate real-time intelligent surveillance has never been higher.
Understanding Ethics, Privacy, and Regulations in Smart Video Surveillance for Public Safety
These systems are used to make the policing and monitoring systems more efficient and improve public safety.
CHAD: Charlotte Anomaly Dataset
In addition to frame-level anomaly labels, CHAD is the first anomaly dataset to include bounding box, identity, and pose annotations for each actor.
Pishgu: Universal Path Prediction Network Architecture for Real-time Cyber-physical Edge Systems
These real-world CPS applications need a robust, lightweight path prediction that can provide a universal network architecture for multiple subjects (e. g., pedestrians and vehicles) from different perspectives.
Ranked #1 on
Trajectory Prediction
on ActEV
A Survey of Graph-based Deep Learning for Anomaly Detection in Distributed Systems
Anomaly detection is a crucial task in complex distributed systems.
A Novel Fully Annotated Thermal Infrared Face Dataset: Recorded in Various Environment Conditions and Distances From The Camera
Our dataset is the first publicly available thermal dataset annotated with the thermal sensation of each subject in different thermal conditions and one of the few datasets in raw 16-bit format.
Machine Learning-Based Automated Thermal Comfort Prediction: Integration of Low-Cost Thermal and Visual Cameras for Higher Accuracy
To that end, a real-time feedback system is needed to provide data about occupants' comfort conditions that can be used to control the building's heating, cooling, and air conditioning (HVAC) system.
ADG-Pose: Automated Dataset Generation for Real-World Human Pose Estimation
Recent advancements in computer vision have seen a rise in the prominence of applications using neural networks to understand human poses.
Real-World Graph Convolution Networks (RW-GCNs) for Action Recognition in Smart Video Surveillance
Action recognition is a key algorithmic part of emerging on-the-edge smart video surveillance and security systems.
DeepTrack: Lightweight Deep Learning for Vehicle Path Prediction in Highways
Vehicle trajectory prediction is essential for enabling safety-critical intelligent transportation systems (ITS) applications used in management and operations.
Single Run Action Detector over Video Stream -- A Privacy Preserving Approach
This paper takes initial strides at designing and evaluating a vision-based system for privacy ensured activity monitoring.
ATCN: Resource-Efficient Processing of Time Series on Edge
This paper presents a scalable deep learning model called Agile Temporal Convolutional Network (ATCN) for high-accurate fast classification and time series prediction in resource-constrained embedded systems.
EfficientHRNet: Efficient Scaling for Lightweight High-Resolution Multi-Person Pose Estimation
The largest model is able to come within 4. 4% accuracy of the current state-of-the-art, while having 1/3 the model size and 1/6 the computation, achieving 23 FPS on Nvidia Jetson Xavier.
CARPe Posterum: A Convolutional Approach for Real-time Pedestrian Path Prediction
Pedestrian path prediction is an essential topic in computer vision and video understanding.
REVAMP$^2$T: Real-time Edge Video Analytics for Multi-camera Privacy-aware Pedestrian Tracking
This article presents REVAMP$^2$T, Real-time Edge Video Analytics for Multi-camera Privacy-aware Pedestrian Tracking, as an integrated end-to-end IoT system for privacy-built-in decentralized situational awareness.
Real-time Person Re-identification at the Edge: A Mixed Precision Approach
In this paper, we study the effect of using a light-weight model, MobileNet-v2 for re-ID and investigate the impact of single (FP32) precision versus half (FP16) precision for training on the server and inference on the edge nodes.
Real-time Deep Learning at the Edge for Scalable Reliability Modeling of Si-MOSFET Power Electronics Converters
This article presents a transformative approach, named Deep Learning Reliability Awareness of Converters at the Edge (Deep RACE), for real-time reliability modeling and prediction of high-frequency MOSFET power electronic converters.
