Author name cluster

Aziz Nasridinov

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

6 papers

1 author row

AAAI Conference 2026 Short Paper

A Lightweight Safety Helmet Compliance Detection via Multimodal Fusion (Student Abstract)

Jeong Hwan Ryu
Azimjon Akhtamov
Md Azher Uddin
Aziz Nasridinov

Ensuring proper use of personal protective equipment (PPE), especially helmets, is essential for workplace safety. Conventional object detectors often fail to distinguish whether a helmet is worn correctly, and existing approaches relying on single-model pipelines are prone to localization errors and false alarms. Moreover, most prior studies do not guarantee real-time performance. To resolve these challenges, we propose a lightweight multimodal approach that integrates a YOLO11-based object detector with a pose estimation model, achieving higher F1 scores and lower false alarm rates while maintaining real-time performance.

PDF Details DOI

AAAI Conference 2024 Short Paper

Solar Power Generation Forecasting via Multimodal Feature Fusion (Student Abstract)

Eul Ka
Seungeun Go
Minjin Kwak
Jeong-Hun Kim
Aziz Nasridinov

Solar power generation has recently been in the spotlight as global warming continues to worsen. However, two significant problems may hinder solar power generation, considering that solar panels are installed outside. The first is soiling, which accumulates on solar panels, and the second is a decrease in sunlight owing to bad weather. In this paper, we will demonstrate that the solar power generation forecasting can increase when considering soiling and sunlight information. We first introduce a dataset containing images of clean and soiled solar panels, sky images, and weather information. For accurate solar power generation forecasting, we propose a new multimodal model that aggregates various features related to weather, soiling, and sunlight. The experimental results demonstrated the high accuracy of our proposed multimodal model.

PDF Details DOI

AAAI Conference 2023 Short Paper

Incremental Density-Based Clustering with Grid Partitioning (Student Abstract)

Jeong-Hun Kim
Tserenpurev Chuluunsaikhan
Jong-Hyeok Choi
Aziz Nasridinov

DBSCAN is widely used in various fields, but it requires computational costs similar to those of re-clustering from scratch to update clusters when new data is inserted. To solve this, we propose an incremental density-based clustering method that rapidly updates clusters by identifying in advance regions where cluster updates will occur. Also, through extensive experiments, we show that our method provides clustering results similar to those of DBSCAN.

PDF Details DOI

AAAI Conference 2020 Short Paper

An Automatic Shoplifting Detection from Surveillance Videos (Student Abstract)

U-Ju Gim
Jae-Jun Lee
Jeong-Hun Kim
Young-Ho Park
Aziz Nasridinov

The use of closed circuit television (CCTV) surveillance devices is increasing every year to prevent abnormal behaviors, including shoplifting. However, damage from shoplifting is also increasing every year. Thus, there is a need for intelligent CCTV surveillance systems that ensure the integrity of shops, despite workforce shortages. In this study, we propose an automatic detection system of shoplifting behaviors from surveillance videos. Instead of extracting features from the whole frame, we use the Region of Interest (ROI) optical- ﬂow fusion network to highlight the necessary features more accurately.

PDF Details

AAAI Conference 2019 Short Paper

A Feasibility Test on Preventing PRMDs Based on Deep Learning

So-Hyun Park
Sun-Young Ihm
Aziz Nasridinov
Young-Ho Park

This study proposes a method to reduce the playing-related musculoskeletal disorders (PRMDs) that often occur among pianists. Specifically, we propose a feasibility test that evaluates several state-of-the-art deep learning algorithms to prevent injuries of pianist. For this, we propose (1) a C3P dataset including various piano playing postures and show (2) the application of four learning algorithms, which demonstrated their superiority in video classification, to the proposed C3P datasets. To our knowledge, this is the first study that attempted to apply the deep learning paradigm to reduce the PRMDs in pianist. The experimental results demonstrated that the classification accuracy is 80% on average, indicating that the proposed hypothesis about the effectiveness of the deep learning algorithms to prevent injuries of pianist is true.

PDF Details

AAAI Conference 2019 Short Paper

AVS-Net: Automatic Visual Surveillance Using Relation Network

Sein Jang
Young-Ho Park
Aziz Nasridinov

Visual surveillance through closed circuit television (CCTV) can help to prevent crime. In this paper, we propose an automatic visual surveillance network (AVS-Net), which simultaneously performs image processing and object detection to determine the dangers of situations captured by CCTV. In addition, we add a relation module to infer the relationships of the objects in the images. Experimental results show that the relation module greatly improves classification accuracy, even if there is not enough information.

PDF Details