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Researcher profile

Hamed Tabkhi

Hamed Tabkhi contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Computer VisionMachine Learningeess.IVArtificial IntelligenceHardware ArchitectureRobotics

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Published work

10 published item(s)

preprint2026arXiv

Intelligent CCTV for Urban Design: AI-Based Analysis of Soft Infrastructure at Intersections

Artificial intelligence (AI) and computer vision are transforming transportation data collection. This study introduces an AI-enabled analytics framework leveraging existing CCTV infrastructure to evaluate the impact of soft interventions, such as temporary pedestrian refuges and curb extensions, on vehicle speed and safety. Using deep learning and perspective-based speed estimation, we evaluated driver behavior before and after interventions, with repeated post-installation monitoring in Week 1 and Week 2, in Minneapolis. Findings reveal that at unsignalized intersections, mean and 85th-percentile speeds fell by up to 18.75% and 16.56%, respectively, while pass-through traffic decreased by as much as 12.2%. Signalized intersections showed comparable reductions except one location, with mean and 85th-percentile speeds dropping by up to 20.0% and 17.19%. These results demonstrate the traffic-calming effectiveness of soft infrastructure and underscore the utility of AI-powered methods for rapid, low-cost, and evidence-based transport policy evaluation.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Novel Fully Annotated Thermal Infrared Face Dataset: Recorded in Various Environment Conditions and Distances From The Camera

Facial thermography is one of the most popular research areas in infrared thermal imaging, with diverse applications in medical, surveillance, and environmental monitoring. However, in contrast to facial imagery in the visual spectrum, the lack of public datasets on facial thermal images is an obstacle to research improvement in this area. Thermal face imagery is still a relatively new research area to be evaluated and studied in different domains.The current thermal face datasets are limited in regards to the subjects' distance from the camera, the ambient temperature variation, and facial landmarks' localization. We address these gaps by presenting a new facial thermography dataset. This article makes two main contributions to the body of knowledge. First, it presents a comprehensive review and comparison of current public datasets in facial thermography. Second, it introduces and studies a novel public dataset on facial thermography, which we call it Charlotte-ThermalFace. Charlotte-ThermalFace contains more than10000 infrared thermal images in varying thermal conditions, several distances from the camera, and different head positions. The data is fully annotated with the facial landmarks, ambient temperature, relative humidity, the air speed of the room, distance to the camera, and subject thermal sensation at the time of capturing each image. 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. Finally, we present a preliminary analysis of the dataset to show the applicability and importance of the thermal conditions in facial thermography. The full dataset, including annotations, are freely available for research purpose at https://github.com/TeCSAR-UNCC/UNCC-ThermalFace

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

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. However, while accuracy has been steadily increasing on State-of-the-Art datasets, these datasets often do not address the challenges seen in real-world applications. These challenges are dealing with people distant from the camera, people in crowds, and heavily occluded people. As a result, many real-world applications have trained on data that does not reflect the data present in deployment, leading to significant underperformance. This article presents ADG-Pose, a method for automatically generating datasets for real-world human pose estimation. These datasets can be customized to determine person distances, crowdedness, and occlusion distributions. Models trained with our method are able to perform in the presence of these challenges where those trained on other datasets fail. Using ADG-Pose, end-to-end accuracy for real-world skeleton-based action recognition sees a 20% increase on scenes with moderate distance and occlusion levels, and a 4X increase on distant scenes where other models failed to perform better than random.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

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. ATCN is a family of compact networks with formalized hyperparameters that enable application-specific adjustments to be made to the model architecture. It is primarily designed for embedded edge devices with very limited performance and memory, such as wearable biomedical devices and real-time reliability monitoring systems. ATCN makes fundamental improvements over the mainstream temporal convolutional neural networks, including residual connections to increase the network depth and accuracy, and the incorporation of separable depth-wise convolution to reduce the computational complexity of the model. As part of the present work, two ATCN families, namely T0, and T1 are also presented and evaluated on different ranges of embedded processors - Cortex-M7 and Cortex-A57 processor. An evaluation of the ATCN models against the best-in-class InceptionTime and MiniRocket shows that ATCN almost maintains accuracy while improving the execution time on a broad range of embedded and cyber-physical applications with demand for real-time processing on the embedded edge. At the same time, in contrast to existing solutions, ATCN is the first time-series classifier based on deep learning that can be run bare-metal on embedded microcontrollers (Cortex-M7) with limited computational performance and memory capacity while delivering state-of-the-art accuracy.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

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. While there have been some promising advances in the field, there is a need for modern deep learning algorithms that allow real-time trajectory prediction on embedded IoT devices. This article presents DeepTrack, a novel deep learning algorithm customized for real-time vehicle trajectory prediction and monitoring applications in arterial management, freeway management, traffic incident management, and work zone management for high-speed incoming traffic. In contrast to previous methods, the vehicle dynamics are encoded using Temporal Convolutional Networks (TCNs) to provide more robust time prediction with less computation. DeepTrack also uses depthwise convolution, which reduces the complexity of models compared to existing approaches in terms of model size and operations. Overall, our experimental results demonstrate that DeepTrack achieves comparable accuracy to state-of-the-art trajectory prediction models but with smaller model sizes and lower computational complexity, making it more suitable for real-world deployment.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Machine Learning-Based Automated Thermal Comfort Prediction: Integration of Low-Cost Thermal and Visual Cameras for Higher Accuracy

Recent research is trying to leverage occupants' demand in the building's control loop to consider individuals' well-being and the buildings' energy savings. 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. The emergence of thermal imaging techniques provides an excellent opportunity for contactless data gathering with no interruption in occupant conditions and activities. There is increasing attention to infrared thermal camera usage in public buildings because of their non-invasive quality in reading the human skin temperature. However, the state-of-the-art methods need additional modifications to become more reliable. To capitalize potentials and address some existing limitations, new solutions are required to bring a more holistic view toward non-intrusive thermal scanning by leveraging the benefit of machine learning and image processing. This research implements an automated approach to collect and register simultaneous thermal and visual images and read the facial temperature in different regions. This paper also presents two additional investigations. First, through utilizing IButton wearable thermal sensors on the forehead area, we investigate the reliability of an in-expensive thermal camera (FLIR Lepton) in reading the skin temperature. Second, by studying the false-color version of thermal images, we look into the possibility of non-radiometric thermal images for predicting personalized thermal comfort. The results shows the strong performance of Random Forest and K-Nearest Neighbor prediction algorithms in predicting personalized thermal comfort. In addition, we have found that non-radiometric images can also indicate thermal comfort when the algorithm is trained with larger amounts of data.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

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. Skeleton-based action recognition is an attractive approach which, instead of using RGB pixel data, relies on human pose information to classify appropriate actions. However, existing algorithms often assume ideal conditions that are not representative of real-world limitations, such as noisy input, latency requirements, and edge resource constraints. To address the limitations of existing approaches, this paper presents Real-World Graph Convolution Networks (RW-GCNs), an architecture-level solution for meeting the domain constraints of Real World Skeleton-based Action Recognition. Inspired by the presence of feedback connections in the human visual cortex, RW-GCNs leverage attentive feedback augmentation on existing near state-of-the-art (SotA) Spatial-Temporal Graph Convolution Networks (ST-GCNs). The ST-GCNs' design choices are derived from information theory-centric principles to address both the spatial and temporal noise typically encountered in end-to-end real-time and on-the-edge smart video systems. Our results demonstrate RW-GCNs' ability to serve these applications by achieving a new SotA accuracy on the NTU-RGB-D-120 dataset at 94.1%, and achieving 32X less latency than baseline ST-GCN applications while still achieving 90.4% accuracy on the Northwestern UCLA dataset in the presence of spatial keypoint noise. RW-GCNs further show system scalability by running on the 10X cost effective NVIDIA Jetson Nano (as opposed to NVIDIA Xavier NX), while still maintaining a respectful range of throughput (15.6 to 5.5 Actions per Second) on the resource constrained device. The code is available here: https://github.com/TeCSAR-UNCC/RW-GCN.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

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. The proposed technology utilizing Artificial Intelligence (AI)-empowered proactive systems offering continuous monitoring, behavioral analysis, and modeling of human activities. To this end, this paper presents Single Run Action Detector (S-RAD) which is a real-time privacy-preserving action detector that performs end-to-end action localization and classification. It is based on Faster-RCNN combined with temporal shift modeling and segment based sampling to capture the human actions. Results on UCF-Sports and UR Fall dataset present comparable accuracy to State-of-the-Art approaches with significantly lower model size and computation demand and the ability for real-time execution on edge embedded device (e.g. Nvidia Jetson Xavier).

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

DeepDive: An Integrative Algorithm/Architecture Co-Design for Deep Separable Convolutional Neural Networks

Deep Separable Convolutional Neural Networks (DSCNNs) have become the emerging paradigm by offering modular networks with structural sparsity in order to achieve higher accuracy with relatively lower operations and parameters. However, there is a lack of customized architectures that can provide flexible solutions that fit the sparsity of the DSCNNs. This paper introduces DeepDive, which is a fully-functional, vertical co-design framework, for power-efficient implementation of DSCNNs on edge FPGAs. DeepDive's architecture supports crucial heterogeneous Compute Units (CUs) to fully support DSCNNs with various convolutional operators interconnected with structural sparsity. It offers an FPGA-aware training and online quantization combined with modular synthesizable C++ CUs, customized for DSCNNs. The execution results on Xilinx's ZCU102 FPGA board, demonstrate 47.4 and 233.3 FPS/Watt for MobileNet-V2 and a compact version of EfficientNet, respectively, as two state-of-the-art depthwise separable CNNs. These comparisons showcase how DeepDive improves FPS/Watt by 2.2$\times$ and 1.51$\times$ over Jetson Nano high and low power modes, respectively. It also enhances FPS/Watt about 2.27$\times$ and 37.25$\times$ over two other FPGA implementations. The DeepDive output for MobileNetV2 is available at https://github.com/TeCSAR-UNCC/DeepDive.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

EfficientHRNet: Efficient Scaling for Lightweight High-Resolution Multi-Person Pose Estimation

There is an increasing demand for lightweight multi-person pose estimation for many emerging smart IoT applications. However, the existing algorithms tend to have large model sizes and intense computational requirements, making them ill-suited for real-time applications and deployment on resource-constrained hardware. Lightweight and real-time approaches are exceedingly rare and come at the cost of inferior accuracy. In this paper, we present EfficientHRNet, a family of lightweight multi-person human pose estimators that are able to perform in real-time on resource-constrained devices. By unifying recent advances in model scaling with high-resolution feature representations, EfficientHRNet creates highly accurate models while reducing computation enough to achieve real-time performance. 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. Compared to the top real-time approach, EfficientHRNet increases accuracy by 22% while achieving similar FPS with 1/3 the power. At every level, EfficientHRNet proves to be more computationally efficient than other bottom-up 2D human pose estimation approaches, while achieving highly competitive accuracy.

0 citations0 reviewsNo code linkedOpen access