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Article
Computer Science, Information Systems
Jing Wang et al.
Summary: This article introduces LogoDet-3K, the largest logo detection dataset with full annotation, and proposes a strong baseline method Logo-Yolo for large-scale logo detection. The dataset and method contribute to promoting research in logo-related fields.
ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Rita Delussu et al.
Summary: This study proposes a method for automatically generating a synthetic image training set for crowd counting in application scenarios where representative images are lacking. Experimental results show that this method can improve the effectiveness of existing crowd counting methods.
PATTERN RECOGNITION
(2022)
Article
M. N. Sharath et al.
International Journal of Information Technology (Singapore)
(2022)
Article
Chemistry, Multidisciplinary
Fadwa Alrowais et al.
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
Automation & Control Systems
M. Kavitha et al.
Summary: With the advancement of communication and computing technologies, multimedia technologies involving video and image applications have become an important part of the information society. This paper presents a novel very deep convolutional neural network model with spatiotemporal similarity for video reconstruction. The model considers the spatiotemporal relationship between video frames and experimental results demonstrate its superior performance.
INTELLIGENT AUTOMATION AND SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Yan-Bo Liu et al.
Summary: The crowd counting method proposed in this study utilizes multiscale convolution and self-attention residual modules to effectively address the issues of occlusion and scale changes in crowd estimation, ultimately improving the accuracy of crowd counting.
APPLIED INTELLIGENCE
(2021)
Article
Automation & Control Systems
Junyu Gao et al.
Summary: With the advancement of deep neural networks, the performance of crowd counting and pixel-wise density estimation has been improving, but challenges persist. A synthetic crowd dataset released recently helps address the difficulty in collecting data, but the domain gap between real data and synthetic images hinders model performance. To tackle this, a domain-adaptation-style crowd counting method is proposed in this article, which effectively adapts the model from synthetic data to specific real-world scenes.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Computer Science, Artificial Intelligence
Fatma Bouhlel et al.
Summary: The research team proposed a new method for crowd density estimation in aerial images, which successfully detected crowded areas showing abnormal densities by fusion of deep and handcrafted features. Divided into offline and inference phases, the method classified aerial image patches into four classes effectively, proving the validity and efficiency of the proposed approach.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Qi Wang et al.
Summary: In the past decade, crowd counting and localization have gained much attention from researchers due to their wide range of applications. The NWPU-Crowd dataset is constructed to address the issue of small-scale datasets, containing a large number of annotated heads with points and boxes. A benchmark website is developed for impartial evaluation of different methods, providing researchers with a platform to submit test results and analyze new challenges in the field.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2021)
Article
Computer Science, Information Systems
Khalil Khan et al.
Summary: Crowd counting is a significant research area with various applications in disaster management systems, public events, safety monitoring, etc. Researchers proposed an end-to-end semantic segmentation framework based on optimized convolutional neural network for crowd counting in dense crowded images, overcoming scale variations through multi-scale features extraction. The proposed algorithm achieved better results compared to previous results in experiments with four standard crowd-counting datasets.
Proceedings Paper
Computer Science, Artificial Intelligence
Lingbo Liu et al.
Summary: Incorporating optical and thermal information is found to help recognize pedestrians, a large-scale RGBT Crowd Counting benchmark and a cross-modal collaborative representation learning framework are introduced for capturing complementary information from different modalities, with extensive experiments demonstrating the effectiveness for RGBT crowd counting.
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021
(2021)
Article
Computer Science, Artificial Intelligence
Liping Zhu et al.
NEURAL COMPUTING & APPLICATIONS
(2020)
Review
Chemistry, Analytical
Naveed Ilyas et al.
Article
Computer Science, Artificial Intelligence
Xinya Chen et al.
Article
Computer Science, Theory & Methods
Shimin Li et al.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2020)
Article
Engineering, Electrical & Electronic
Usman Sajid et al.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2020)
Article
Computer Science, Artificial Intelligence
Yunqi Miao et al.
PATTERN RECOGNITION LETTERS
(2019)
Proceedings Paper
Imaging Science & Photographic Technology
Sultan Daud Khan et al.
2019 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
(2019)
Article
Computer Science, Artificial Intelligence
Vishwanath A. Sindagi et al.
PATTERN RECOGNITION LETTERS
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Lokesh Boominathan et al.
MM'16: PROCEEDINGS OF THE 2016 ACM MULTIMEDIA CONFERENCE
(2016)
