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Computer Science, Software Engineering
Hideo Bannai, I Tomohiro, Tomasz Kociumaka, Dominik Koeppl, Simon J. Puglisi
Summary: The article proposes two algorithms. One algorithm finds the longest Lyndon subsequence of a string T in O(n(3)) time complexity and O(n) space complexity, while the other algorithm finds the longest Lyndon subsequence online in O(n(3)) time complexity and space complexity. The first algorithm can also be extended to find the longest common Lyndon subsequence of two strings in O (n(4 )s) time complexity using O(n(2)) space complexity.
Article
Computer Science, Artificial Intelligence
Jose D. Fernandez-Rodriguez, Jorge Garcia-Gonzalez, Rafaela Benitez-Rochel, Miguel A. Molina-Cabello, Gonzalo Ramos-Jimenez, Ezequiel Lopez-Rubio
Summary: This study proposes a model for automatically detecting anomalous vehicle trajectories using video sequences from traffic cameras. The model detects vehicles frame by frame, tracks their trajectories, estimates velocity vectors, and compares them to neighboring trajectories. Vehicles in wrong-way trajectories can be detected with this strategy.
INTEGRATED COMPUTER-AIDED ENGINEERING
(2023)
Article
Computer Science, Information Systems
Ye Ding, Wenyi Zhang, Xibo Zhou, Qing Liao, Qiong Luo, Lionel M. Ni
Summary: The article introduces a system called FraudTrip, which effectively and efficiently detects unmetered taxi trips, solving the problem that existing detection methods cannot be applied to real-world scenarios.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Artificial Intelligence
Thi Phuong Quyen Nguyen, Phan Nguyen Ky Phuc, Chao -Lung Yang, Hendri Sutrisno, Bao-Han Luong, Thi Huynh Anh Le, Thanh Tung Nguyen
Summary: This study proposes a novel approach for time-series anomaly detection using the longest common subsequence (LCS) problem. The proposed algorithms, extent FGLCS and MDP-LCS, are designed to handle real-time series data and achieve improved computational efficiency compared to existing methods.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Biochemical Research Methods
Changyong Yu, Pengxi Lin, Yuhai Zhao, Tianmei Ren, Guoren Wang
Summary: In this study, a mini Directed Acyclic Graph (mini-DAG) model and a novel Path Elimination Algorithm were proposed to efficiently solve large-scale MLCS problems.
BMC BIOINFORMATICS
(2022)
Review
Computer Science, Interdisciplinary Applications
Bhawana Tyagi, Swati Nigam, Rajiv Singh
Summary: In today's crowded events, the possibility of suspicious activities and violence increases. Developing automated systems for crowd analysis is important to ensure public safety and prevent disasters. This analysis involves density estimation, crowd counting, object recognition, tracking, and anomaly detection. Comparing different methods and creating taxonomies for crowd analysis are also important for effective implementation.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2022)
Article
Computer Science, Information Systems
Martha Dais Ferreira, Jessica Campbell, Evan Purney, Amilcar Soares, Stan Matwin
Summary: In the maritime environment, the Automatic Identification System (AIS) is used to monitor vessel activity for security and safety reasons. AIS data helps detect anomalous behaviors related to suspicious activities and hazardous events. However, clustering analysis of AIS data faces challenges in determining dissimilarity between trajectories and in computational costs. To address this, compression algorithms can be applied to reduce processing time while preserving clustering results. The analysis shows that suitable compression algorithms can effectively reduce processing time without significant impact on clustering results and support scalability.
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
(2023)
Review
Chemistry, Analytical
Amnah Aldayri, Waleed Albattah
Summary: This paper provides a detailed review of recent developments in anomaly detection methods from the perspective of computer vision, based on different available datasets. A new taxonomic organization of existing works in crowd analysis and anomaly detection is introduced. A summary of existing reviews and datasets related to anomaly detection is listed, covering an overview of different crowd concepts, types of anomalies, and surveillance systems. Additionally, research trends and future work prospects are analyzed.
Article
Chemistry, Multidisciplinary
Alberto Blazquez-Herranz, Juan-Ignacio Caballero-Garzon, Albert Zilverberg, Christian Wolff, Alejandro Rodriguez-Gonzalez, Ernestina Menasalvas
Summary: Mobile devices with sensors generate geo-spatial data for future applications, with trajectory clustering being crucial for analyzing common point events. The CROSS-CPP project aims to provide tools for data storage and analysis, with an adapted Quickbundles algorithm showing superior performance in trajectory clustering experiments using various distance measures.
APPLIED SCIENCES-BASEL
(2021)
Article
Engineering, Electrical & Electronic
Xingfa Shen, Chuang Li, Weijie Chen, Yongcai Wang
Summary: This paper proposes a MapICT scheme that utilizes low-quality trajectory data to construct a radio map. The proposed scheme employs the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm to extract target fingerprint vertexes, balances the distribution of trajectory data, and extracts target edges using inertial data, thereby constructing a two-dimensional radio map. Simulation experiments and experiments in actual environments demonstrate the feasibility and effectiveness of the MapICT scheme, which achieves higher locating accuracy compared to existing methods, with a 11.98% improvement in a teaching building and a 7.38% improvement in a mall.
IEEE SENSORS JOURNAL
(2022)
Article
Computer Science, Information Systems
J. Fumanal-Idocin, I Rodriguez-Martinez, A. Indurain, M. Minarova, H. Bustince
Summary: This paper proposes a simulation-based anomaly detection algorithm that identifies abnormal observations significantly different from normal ones using the aggregation of gravitational forces and cluster analysis, without prior knowledge or data labels.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Software Engineering
Ashish Singh Patel, Ranjana Vyas, O. P. Vyas, Muneendra Ojha, Vivek Tiwari
Summary: The study introduces a multi-object tracking algorithm to improve crowd management and recognition of unacceptable behavior in public places. It tracks objects through bounding box detection and linear velocity estimation, and manages ID switches by considering motion direction. Additionally, it proposes new methods for detecting loitering behavior and physical distance violation, and calculates actual physical distance using a mathematical approach.
Article
Chemistry, Multidisciplinary
Fadwa Alrowais, Saud S. Alotaibi, Fahd N. Al-Wesabi, Noha Negm, Rana Alabdan, Radwa Marzouk, Amal S. Mehanna, Mesfer Al Duhayyim
Summary: This paper proposes a Metaheuristics with Deep Transfer Learning Enabled Intelligent Crowd Density Detection and Classification (MDTL-ICDDC) model for video surveillance systems. The MDTL-ICDDC model primarily leverages a Salp Swarm Algorithm (SSA) for feature extraction, a weighted extreme learning machine (WELM) for crowd density and classification, and the krill swarm algorithm (KSA) for parameter optimization. Experimental results show that the MDTL-ICDDC system outperforms other models in terms of performance.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Software Engineering
Ye Liu, Shuohong Wang, Jianhui Nie, Hao Gao
Summary: This paper presents a method for detecting and tracking a large number of densely aggregated microbes with arbitrary orientations in image sequences captured under a microscope. By proposing an ICF-based detector and refining detection results through a data association process, the kinematic pattern of microbes is accurately modeled and used to select true targets and match them across frames effectively. Experimental results demonstrate the effectiveness of the proposed method.
Article
Computer Science, Information Systems
Jialiang Zhang, Lixiang Lin, Jianke Zhu, Yang Li, Yun-chen Chen, Yao Hu, Steven C. H. Hoi
Summary: The study introduces an attribute-aware pedestrian detector that models people's semantic attributes explicitly and utilizes attribute-feature-based Non-Maximum Suppression to improve pedestrian detection accuracy in dense environments. Additionally, an enhanced ground truth target is designed to alleviate the class imbalance issue during training.
IEEE TRANSACTIONS ON MULTIMEDIA
(2021)
Article
Engineering, Electrical & Electronic
Srikrishna Karanam, Ziyan Wu, Richard J. Radke
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2018)
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Engineering, Electrical & Electronic
Octavia Camps, Mengran Gou, Tom Hebble, Srikrishna Karanam, Oliver Lehmann, Yang Li, Richard J. Radke, Ziyan Wu, Fei Xiong
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2017)
Article
Computer Science, Artificial Intelligence
Srikrishna Karanam, Mengran Gou, Ziyan Wu, Angels Rates-Borras, Octavia Camps, Richard J. Radke
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2019)
Article
Chemistry, Multidisciplinary
Yuan-Chih Peng, Shuyang Chen, Devavrat Jivani, John Wason, William Lawler, Glenn Saunders, Richard J. Radke, Jeff Trinkle, Shridhar Nath, John T. Wen
Summary: This paper presents a robotic assembly methodology for the manufacturing of large segmented composite structures, which uses sensors and cameras for high-precision panel pick-up, placement, and transport. Human-assisted path planning ensures reliable motion of the robot, demonstrating the versatility of sensor-guided robotic assembly in complex tasks.
APPLIED SCIENCES-BASEL
(2021)
Article
Engineering, Electrical & Electronic
Lingyu Zhang, Richard J. Radke
IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING
(2020)
Proceedings Paper
Computer Science, Cybernetics
Indrani Bhattacharya, Michael Foley, Christine Ku, Ni Zhang, Tongtao Zhang, Cameron Mine, Manling Li, Heng Ji, Christoph Riedl, Brooke Foucault Welles, Richard J. Radke
PROCEEDINGS OF THE 10TH ACM MULTIMEDIA SYSTEMS CONFERENCE (ACM MMSYS'19)
(2019)
Proceedings Paper
Computer Science, Theory & Methods
Lingyu Zhang, Mallory Morgan, Indrani Bhattacharya, Michael Foley, Jonas Braasch, Christoph Riedl, Brooke Foucault Welles, Richard J. Radke
ICMI'19: PROCEEDINGS OF THE 2019 INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION
(2019)
Proceedings Paper
Computer Science, Artificial Intelligence
Manling Li, Lingyu Zhang, Heng Ji, Richard J. Radke
57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019)
(2019)
Proceedings Paper
Computer Science, Cybernetics
Indrani Bhattacharya, Michael Foley, Ni Zhang, Tongtao Zhang, Christine Ku, Cameron Mine, Heng Ji, Christoph Riedl, Brooke Foucault Welles, Richard J. Radke
ICMI'18: PROCEEDINGS OF THE 20TH ACM INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Meng Zheng, Srikrishna Karanam, Richard J. Radke
PROCEEDINGS 2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW)
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Ashraful Islam, Yuexi Zhang, Dong Yin, Octavia Camps, Richard J. Radke
PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON DISTRIBUTED SMART CAMERAS (ICDSC'18)
(2018)
Proceedings Paper
Automation & Control Systems
Daniel Kruse, Richard J. Radke, John T. Wen
2017 AMERICAN CONTROL CONFERENCE (ACC)
(2017)
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Computer Science, Artificial Intelligence
Indrani Bhattacharya, Noam Eshed, Richard J. Radke
2017 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW)
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Mengran Gou, Srikrishna Karanam, Wenqian Liu, Octavia Camps, Richard J. Radke
2017 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW)
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Computer Science, Artificial Intelligence
Indrani Bhattacharya, Richard J. Radke
2016 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2016)
(2016)