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Deep Learning based Object Detection and Tracking in Videos

Submission Deadline: 31 December 2023 (closed) View: 14

Guest Editors

Dr. Sultan Daud Khan, National University of Technology, Pakistan.
Prof. Saleh Basalamah, Umm Al-Qura University, Saudi Arabia.
Dr. Farhan Riaz, University of Lincoln, UK.

Summary

Object detection and tracking in videos has become an increasingly important area of research due to its potential applications in a variety of domains such as video surveillance, autonomous driving, robotics, and healthcare. With the growing popularity of deep learning techniques, computer vision researchers have made significant strides in developing novel approaches for object detection and tracking.


This special issue will provide a platform for researchers to present their latest findings, exchange ideas, and discuss challenges related to object detection and tracking in videos. We invite original research articles, reviews, and surveys related to this topic. Additionally, this issue will also welcome topics on action recognition, anomaly detection, and behavior understanding in videos. The topics of interest include, but are not limited to:

• Deep learning-based object detection and tracking in videos.

• Multi-object tracking in complex scenes.

• Object tracking in low-resolution and low-light conditions.

• Real-time object detection and tracking.

• Feature extraction and selection techniques for object detection and tracking.

• Object detection and tracking in 3D videos.

• Object detection and tracking for augmented reality applications.

• Transfer learning for object detection and tracking in videos.

• Applications of object detection and tracking in videos.

• Small object detection.

• Rotated object detection.


We anticipate that this special issue will attract submissions from leading researchers and experts in the field, and will provide a comprehensive overview of the latest advancements in object detection and tracking in videos.


Keywords

• object detection
• object tracking
• computer vision
• action recognition
• behavior understanding
• anomaly detection
• deep learning

Published Papers


  • Open Access

    ARTICLE

    E2E-MFERC: A Multi-Face Expression Recognition Model for Group Emotion Assessment

    Lin Wang, Juan Zhao, Hu Song, Xiaolong Xu
    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1105-1135, 2024, DOI:10.32604/cmc.2024.048688
    (This article belongs to the Special Issue: Deep Learning based Object Detection and Tracking in Videos)
    Abstract In smart classrooms, conducting multi-face expression recognition based on existing hardware devices to assess students’ group emotions can provide educators with more comprehensive and intuitive classroom effect analysis, thereby continuously promoting the improvement of teaching quality. However, most existing multi-face expression recognition methods adopt a multi-stage approach, with an overall complex process, poor real-time performance, and insufficient generalization ability. In addition, the existing facial expression datasets are mostly single face images, which are of low quality and lack specificity, also restricting the development of this research. This paper aims to propose an end-to-end high-performance multi-face… More >

  • Open Access

    ARTICLE

    YOLOv5ST: A Lightweight and Fast Scene Text Detector

    Yiwei Liu, Yingnan Zhao, Yi Chen, Zheng Hu, Min Xia
    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 909-926, 2024, DOI:10.32604/cmc.2024.047901
    (This article belongs to the Special Issue: Deep Learning based Object Detection and Tracking in Videos)
    Abstract Scene text detection is an important task in computer vision. In this paper, we present YOLOv5 Scene Text (YOLOv5ST), an optimized architecture based on YOLOv5 v6.0 tailored for fast scene text detection. Our primary goal is to enhance inference speed without sacrificing significant detection accuracy, thereby enabling robust performance on resource-constrained devices like drones, closed-circuit television cameras, and other embedded systems. To achieve this, we propose key modifications to the network architecture to lighten the original backbone and improve feature aggregation, including replacing standard convolution with depth-wise convolution, adopting the C2 sequence module in place More >

  • Open Access

    ARTICLE

    IR-YOLO: Real-Time Infrared Vehicle and Pedestrian Detection

    Xiao Luo, Hao Zhu, Zhenli Zhang
    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2667-2687, 2024, DOI:10.32604/cmc.2024.047988
    (This article belongs to the Special Issue: Deep Learning based Object Detection and Tracking in Videos)
    Abstract Road traffic safety can decrease when drivers drive in a low-visibility environment. The application of visual perception technology to detect vehicles and pedestrians in infrared images proves to be an effective means of reducing the risk of accidents. To tackle the challenges posed by the low recognition accuracy and the substantial computational burden associated with current infrared pedestrian-vehicle detection methods, an infrared pedestrian-vehicle detection method A proposal is presented, based on an enhanced version of You Only Look Once version 5 (YOLOv5). First, A head specifically designed for detecting small targets has been integrated into… More >

  • Open Access

    ARTICLE

    Target Detection Algorithm in Foggy Scenes Based on Dual Subnets

    Yuecheng Yu, Liming Cai, Anqi Ning, Jinlong Shi, Xudong Chen, Shixin Huang
    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1915-1931, 2024, DOI:10.32604/cmc.2024.046125
    (This article belongs to the Special Issue: Deep Learning based Object Detection and Tracking in Videos)
    Abstract Under the influence of air humidity, dust, aerosols, etc., in real scenes, haze presents an uneven state. In this way, the image quality and contrast will decrease. In this case, It is difficult to detect the target in the image by the universal detection network. Thus, a dual subnet based on multi-task collaborative training (DSMCT) is proposed in this paper. Firstly, in the training phase, the Gated Context Aggregation Network (GCANet) is used as the supervisory network of YOLOX to promote the extraction of clean information in foggy scenes. In the test phase, only the… More >

  • Open Access

    ARTICLE

    Leveraging Augmented Reality, Semantic-Segmentation, and VANETs for Enhanced Driver’s Safety Assistance

    Sitara Afzal, Imran Ullah Khan, Irfan Mehmood, Jong Weon Lee
    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1443-1460, 2024, DOI:10.32604/cmc.2023.046707
    (This article belongs to the Special Issue: Deep Learning based Object Detection and Tracking in Videos)
    Abstract Overtaking is a crucial maneuver in road transportation that requires a clear view of the road ahead. However, limited visibility of ahead vehicles can often make it challenging for drivers to assess the safety of overtaking maneuvers, leading to accidents and fatalities. In this paper, we consider atrous convolution, a powerful tool for explicitly adjusting the field-of-view of a filter as well as controlling the resolution of feature responses generated by Deep Convolutional Neural Networks in the context of semantic image segmentation. This article explores the potential of seeing-through vehicles as a solution to enhance… More >

  • Open Access

    ARTICLE

    Design of a Lightweight Compressed Video Stream-Based Patient Activity Monitoring System

    Sangeeta Yadav, Preeti Gulia, Nasib Singh Gill, Piyush Kumar Shukla, Arfat Ahmad Khan, Sultan Alharby, Ahmed Alhussen, Mohd Anul Haq
    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1253-1274, 2024, DOI:10.32604/cmc.2023.042869
    (This article belongs to the Special Issue: Deep Learning based Object Detection and Tracking in Videos)
    Abstract Inpatient falls from beds in hospitals are a common problem. Such falls may result in severe injuries. This problem can be addressed by continuous monitoring of patients using cameras. Recent advancements in deep learning-based video analytics have made this task of fall detection more effective and efficient. Along with fall detection, monitoring of different activities of the patients is also of significant concern to assess the improvement in their health. High computation-intensive models are required to monitor every action of the patient precisely. This requirement limits the applicability of such networks. Hence, to keep the… More >

  • Open Access

    ARTICLE

    Zero-DCE++ Inspired Object Detection in Less Illuminated Environment Using Improved YOLOv5

    Ananthakrishnan Balasundaram, Anshuman Mohanty, Ayesha Shaik, Krishnadoss Pradeep, Kedalu Poornachary Vijayakumar, Muthu Subash Kavitha
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2751-2769, 2023, DOI:10.32604/cmc.2023.044374
    (This article belongs to the Special Issue: Deep Learning based Object Detection and Tracking in Videos)
    Abstract Automated object detection has received the most attention over the years. Use cases ranging from autonomous driving applications to military surveillance systems, require robust detection of objects in different illumination conditions. State-of-the-art object detectors tend to fare well in object detection during daytime conditions. However, their performance is severely hampered in night light conditions due to poor illumination. To address this challenge, the manuscript proposes an improved YOLOv5-based object detection framework for effective detection in unevenly illuminated nighttime conditions. Firstly, the preprocessing strategies involve using the Zero-DCE++ approach to enhance lowlight images. It is followed… More >

  • Open Access

    ARTICLE

    Detection of Safety Helmet-Wearing Based on the YOLO_CA Model

    Xiaoqin Wu, Songrong Qian, Ming Yang
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3349-3366, 2023, DOI:10.32604/cmc.2023.043671
    (This article belongs to the Special Issue: Deep Learning based Object Detection and Tracking in Videos)
    Abstract Safety helmets can reduce head injuries from object impacts and lower the probability of safety accidents, as well as being of great significance to construction safety. However, for a variety of reasons, construction workers nowadays may not strictly enforce the rules of wearing safety helmets. In order to strengthen the safety of construction site, the traditional practice is to manage it through methods such as regular inspections by safety officers, but the cost is high and the effect is poor. With the popularization and application of construction site video monitoring, manual video monitoring has been… More >

  • Open Access

    ARTICLE

    Infrared Small Target Detection Algorithm Based on ISTD-CenterNet

    Ning Li, Shucai Huang, Daozhi Wei
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3511-3531, 2023, DOI:10.32604/cmc.2023.045987
    (This article belongs to the Special Issue: Deep Learning based Object Detection and Tracking in Videos)
    Abstract This paper proposes a real-time detection method to improve the Infrared small target detection CenterNet (ISTD-CenterNet) network for detecting small infrared targets in complex environments. The method eliminates the need for an anchor frame, addressing the issues of low accuracy and slow speed. HRNet is used as the framework for feature extraction, and an ECBAM attention module is added to each stage branch for intelligent identification of the positions of small targets and significant objects. A scale enhancement module is also added to obtain a high-level semantic representation and fine-resolution prediction map for the entire… More >

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