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Advanced Computer Vision Techniques: AI-Based Object Detection, Tracking, Surveillance and Security Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 February 2026 | Viewed by 5814

Special Issue Editors

Dr. Deokwoo Lee

E-Mail Website
Guest Editor
Department of Computer Engineering, Keimyung University, Daegu 704-701, Republic of Korea
Interests: camera calibration; computer vision; image processing; signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Yohan Park

E-Mail Website
Guest Editor
Next-Generation Information Security Laboratory (NISL), College of Engineering, Keimyung University, Daegu 24601, Republic of Korea
Interests: network security; security of IoT; blockchain; post-quantum cryptography; ITS; formal analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore advanced approaches in computer vision, focusing on AI-based object detection, tracking, surveillance, and security applications. We welcome the submission of papers related to image-, text-, or multimodal-based applications in computer vision and image processing from theoretical and practical perspectives. In particular, this Special Issue encourages the submission of papers focused on practical areas such as vehicles, bio-medical engineering, surveillance, etc., that outline the latest industrial and research trends. As artificial intelligence (AI) has brought about significant improvements in many aspects of human life, diverse approaches using AI techniques are of particular interest to this Special Issue. Contributions on practical implementations in areas like public safety, surveillance systems, and intelligent security systems are also encouraged. In addition to the performance of AI-based detection, tracking, recognition, etc., approaches to efficient AI models, e.g., lightweight deep learning models, are also of interest to this Special Issue.

Dr. Deokwoo Lee
Dr. YoHan Park
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • detection
  • tracking
  • recognition
  • information security
  • computer networks
  • deep learning
  • lightweight model

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Published Papers (4 papers)

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Research

19 pages, 2964 KB  
Article
Towards Occlusion-Aware Multi-Pedestrian Tracking
by Hechuang Wang, Tong Chen and Yifan Wang
Appl. Sci. 2025, 15(24), 13045; https://doi.org/10.3390/app152413045 - 11 Dec 2025
Viewed by 218
Abstract
Achieving robust multi-object tracking in complex real-world scenarios remains a challenging task. Existing approaches often struggle to effectively handle occlusion, primarily because occlusion can result in unreliable appearance features, inaccurate motion estimation, and biased association cues. To address these challenges, this study proposes [...] Read more.
Achieving robust multi-object tracking in complex real-world scenarios remains a challenging task. Existing approaches often struggle to effectively handle occlusion, primarily because occlusion can result in unreliable appearance features, inaccurate motion estimation, and biased association cues. To address these challenges, this study proposes OATrack, a pedestrian multi-object tracking framework with explicit occlusion awareness. First, an occlusion perception module is introduced to estimate the occlusion rate and provide it as input for subsequent components. Subsequently, the Kalman Filter’s innovation gain is adaptively suppressed according to the target’s occlusion level, and association cues are assigned adaptive weights based on occlusion severity. Experimental results on the MOT17 benchmark dataset demonstrate that the proposed method achieves state-of-the-art performance in key tracking metrics. Specifically, on the MOT17 test set, the method achieves an IDF1 score of 80.6% and a HOTA score of 65.3%. On the MOT20 test set, it attains an IDF1 of 77.8% and a HOTA of 63.6%. The proposed algorithm offers an effective solution for multi-object tracking in environments characterized by frequent and complex occlusions. Full article
(This article belongs to the Special Issue Advanced Computer Vision Techniques: AI-Based Object Detection, Tracking, Surveillance and Security Applications)
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18 pages, 2013 KB  
Article
Deep Learning-Based Human Activity Recognition Using Dilated CNN and LSTM on Video Sequences of Various Actions Dataset
by Bakht Alam Khan and Jin-Woo Jung
Appl. Sci. 2025, 15(22), 12173; https://doi.org/10.3390/app152212173 - 17 Nov 2025
Viewed by 527
Abstract
Human Activity Recognition (HAR) plays a critical role across various fields, including surveillance, healthcare, and robotics, by enabling systems to interpret and respond to human behaviors. In this research, we present an innovative method for HAR that leverages the strengths of Dilated Convolutional [...] Read more.
Human Activity Recognition (HAR) plays a critical role across various fields, including surveillance, healthcare, and robotics, by enabling systems to interpret and respond to human behaviors. In this research, we present an innovative method for HAR that leverages the strengths of Dilated Convolutional Neural Networks (CNNs) integrated with Long Short-Term Memory (LSTM) networks. The proposed architecture achieves an impressive accuracy of 94.9%, surpassing the conventional CNN-LSTM approach, which achieves 93.7% accuracy on the challenging UCF 50 dataset. The use of dilated CNNs significantly enhances the model’s ability to capture extensive spatial–temporal features by expanding the receptive field, thus enabling the recognition of intricate human activities. This approach effectively preserves fine-grained details without increasing computational costs. The inclusion of LSTM layers further strengthens the model’s performance by capturing temporal dependencies, allowing for a deeper understanding of action sequences over time. To validate the robustness of our model, we assessed its generalization capabilities on an unseen YouTube video, demonstrating its adaptability to real-world applications. The superior performance and flexibility of our approach suggests its potential to advance HAR applications in areas like surveillance, human–computer interaction, and healthcare monitoring. Full article
(This article belongs to the Special Issue Advanced Computer Vision Techniques: AI-Based Object Detection, Tracking, Surveillance and Security Applications)
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26 pages, 8770 KB  
Article
Evaluation of Benchmark Datasets and Deep Learning Models with Pre-Trained Weights for Vision-Based Dynamic Hand Gesture Recognition
by Yaseen, Oh-Jin Kwon, Jaeho Kim, Jinhee Lee and Faiz Ullah
Appl. Sci. 2025, 15(11), 6045; https://doi.org/10.3390/app15116045 - 27 May 2025
Cited by 3 | Viewed by 3443
Abstract
The integration of dynamic hand gesture recognition in computer vision-based systems promises enhanced human–computer interaction, providing a natural and intuitive way of communicating. However, achieving real-time performance efficiency is a highly challenging task. As the effectiveness of dynamic hand gesture recognition is dependent [...] Read more.
The integration of dynamic hand gesture recognition in computer vision-based systems promises enhanced human–computer interaction, providing a natural and intuitive way of communicating. However, achieving real-time performance efficiency is a highly challenging task. As the effectiveness of dynamic hand gesture recognition is dependent on the nature of the underlying datasets and deep learning models, selecting a diverse and effective dataset and a deep learning model is crucial to achieve reliable performance. This study explores the effectiveness of benchmark hand gesture recognition datasets in training lightweight deep learning models for robust performance. The objective is to evaluate and analyze these datasets and models through training and evaluation for use in practical applications. For the evaluation of these datasets and models, we analyze the models’ performances by evaluation metrics, such as precision, recall, F1-score, specificity, and accuracy. For an unbiased comparison, both subjective and objective metrics are reported, thus offering significant insights on understanding dataset–model interactions in hand gesture recognition. Full article
(This article belongs to the Special Issue Advanced Computer Vision Techniques: AI-Based Object Detection, Tracking, Surveillance and Security Applications)
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15 pages, 19341 KB  
Article
SMILE: Segmentation-Based Centroid Matching for Image Rectification via Aligning Epipolar Lines
by Junewoo Choi and Deokwoo Lee
Appl. Sci. 2025, 15(9), 4962; https://doi.org/10.3390/app15094962 - 30 Apr 2025
Viewed by 983
Abstract
Stereo images, which consist of left and right image pairs, are often unaligned when initially captured, as they represent raw data. Stereo images are typically used in scenarios requiring disparity between the left and right views, such as depth estimation. In such cases, [...] Read more.
Stereo images, which consist of left and right image pairs, are often unaligned when initially captured, as they represent raw data. Stereo images are typically used in scenarios requiring disparity between the left and right views, such as depth estimation. In such cases, image calibration is performed to obtain the necessary parameters, and, based on these parameters, image rectification is applied to align the epipolar lines of the stereo images. This preprocessing step is crucial for effectively utilizing stereo images. The conventional method for performing image calibration usually involves using a reference object, such as a checkerboard, to obtain these parameters. In this paper, we propose a novel approach that does not require any special reference points like a checkerboard. Instead, we employ object detection to segment object pairs and calculate the centroids of the segmented objects. By aligning the y-coordinates of these centroids in the left and right image pairs, we induce the epipolar lines to be parallel, achieving an effect similar to image rectification. Full article
(This article belongs to the Special Issue Advanced Computer Vision Techniques: AI-Based Object Detection, Tracking, Surveillance and Security Applications)
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