Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (101)
  • Open Access


    A Real Time Vision-Based Smoking Detection Framework on Edge

    Ruilong Chen1, Guangfu Zeng1, Ke Wang2, Lei Luo1,*, Zhiping Cai1

    Journal on Internet of Things, Vol.2, No.2, pp. 55-64, 2020, DOI:10.32604/jiot.2020.09814

    Abstract Smoking is the main reason for fire disaster and pollution in petrol station, construction site and warehouse. Existing solutions based on wearable devices and smoking sensors were costly and hard to obtain evidence of smoking in unmanned scenarios. With the developments of closed circuit television (CCTV) system, vision-based methods for object detection, mostly driven by deep learning techniques, were introduced recently. However, the massive GPU computing hardware required by the deep learning algorithm made these methods hard to be deployed. This paper aims at solving the smoking detection problem on edge and proposes the solution that has fast detection speed,… More >

  • Open Access


    Visual Object Detection and Tracking Using Analytical Learning Approach of Validity Level

    Yong‐Hwan Lee, Hyochang Ahn, Hyo‐Beom Ahn, Sun‐Young Lee

    Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 205-215, 2019, DOI:10.31209/2018.100000056

    Abstract Object tracking plays an important role in many vision applications. This paper proposes a novel and robust object detection and tracking method to localize and track a visual object in video stream. The proposed method is consisted of three modules; object detection, tracking and learning. Detection module finds and localizes all apparent objects, corrects the tracker if necessary. Tracking module follows the interest object by every frame of sequences. Learning module estimates a detecting error, and updates its value of credibility level. With a validity level where the tracking is failed on tracing the learned object, detection module finds again… More >

  • Open Access


    Vehicle Target Detection Method Based on Improved SSD Model

    Guanghui Yu1, Honghui Fan1, Hongyan Zhou1, Tao Wu1, Hongjin Zhu1, *

    Journal on Artificial Intelligence, Vol.2, No.3, pp. 125-135, 2020, DOI:10.32604/jai.2020.010501

    Abstract When we use traditional computer vision Inspection technology to locate the vehicles, we find that the results were unsatisfactory, because of the existence of diversified scenes and uncertainty. So, we present a new method based on improved SSD model. We adopt ResNet101 to enhance the feature extraction ability of algorithm model instead of the VGG16 used by the classic model. Meanwhile, the new method optimizes the loss function, such as the loss function of predicted offset, and makes the loss function drop more smoothly near zero points. In addition, the new method improves cross entropy loss function of category prediction,… More >

  • Open Access


    A Review of Object Detectors in Deep Learning

    Chen Song1, Xu Cheng1, *, Yongxiang Gu1, Beijing Chen1, Zhangjie Fu1

    Journal on Artificial Intelligence, Vol.2, No.2, pp. 59-77, 2020, DOI:10.32604/jai.2020.010193

    Abstract Object detection is one of the most fundamental, longstanding and significant problems in the field of computer vision, where detection involves object classification and location. Compared with the traditional object detection algorithms, deep learning makes full use of its powerful feature learning capabilities showing better detection performance. Meanwhile, the emergence of large datasets and tremendous improvement in computer computing power have also contributed to the vigorous development of this field. In the paper, many aspects of generic object detection are introduced and summarized such as traditional object detection algorithms, datasets, evaluation metrics, detection frameworks based on deep learning and state-of-the-art… More >

  • Open Access


    Using Object Detection Network for Malware Detection and Identification in Network Traffic Packets

    Chunlai Du1, Shenghui Liu1, Lei Si2, Yanhui Guo2, *, Tong Jin1

    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1785-1796, 2020, DOI:10.32604/cmc.2020.010091

    Abstract In recent years, the number of exposed vulnerabilities has grown rapidly and more and more attacks occurred to intrude on the target computers using these vulnerabilities such as different malware. Malware detection has attracted more attention and still faces severe challenges. As malware detection based traditional machine learning relies on exports’ experience to design efficient features to distinguish different malware, it causes bottleneck on feature engineer and is also time-consuming to find efficient features. Due to its promising ability in automatically proposing and selecting significant features, deep learning has gradually become a research hotspot. In this paper, aiming to detect… More >

  • Open Access


    An Improved Non-Parametric Method for Multiple Moving Objects Detection in the Markov Random Field

    Qin Wan1,2,*, Xiaolin Zhu1, Yueping Xiao1, Jine Yan1, Guoquan Chen1, Mingui Sun3

    CMES-Computer Modeling in Engineering & Sciences, Vol.124, No.1, pp. 129-149, 2020, DOI:10.32604/cmes.2020.09397

    Abstract Detecting moving objects in the stationary background is an important problem in visual surveillance systems. However, the traditional background subtraction method fails when the background is not completely stationary and involves certain dynamic changes. In this paper, according to the basic steps of the background subtraction method, a novel non-parametric moving object detection method is proposed based on an improved ant colony algorithm by using the Markov random field. Concretely, the contributions are as follows: 1) A new nonparametric strategy is utilized to model the background, based on an improved kernel density estimation; this approach uses an adaptive bandwidth, and… More >

  • Open Access


    A Novel Steganography Algorithm Based on Instance Segmentation

    Ruohan Meng1, 2, Qi Cui1, 2, Zhili Zhou1, 2, Chengsheng Yuan1, 2, 3, Xingming Sun1, 2, *

    CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 183-196, 2020, DOI:10.32604/cmc.2020.05317

    Abstract Information hiding tends to hide secret information in image area where is rich texture or high frequency, so as to transmit secret information to the recipient without affecting the visual quality of the image and arousing suspicion. We take advantage of the complexity of the object texture and consider that under certain circumstances, the object texture is more complex than the background of the image, so the foreground object is more suitable for steganography than the background. On the basis of instance segmentation, such as Mask R-CNN, the proposed method hides secret information into each object's region by using the… More >

  • Open Access


    Intelligent Mobile Drone System Based on Real-Time Object Detection

    Chuanlong Li1,2, Xingming Sun1,2,*, Junhao Cai3,*

    Journal on Artificial Intelligence, Vol.1, No.1, pp. 1-8, 2019, DOI:10.32604/jai.2019.06064

    Abstract Drone also known as unmanned aerial vehicle (UAV) has drawn lots of attention in recent years. Quadcopter as one of the most popular drones has great potential in both industrial and academic fields. Quadcopter drones are capable of taking off vertically and flying towards any direction. Traditional researches of drones mainly focus on their mechanical structures and movement control. The aircraft movement is usually controlled by a remote controller manually or the trajectory is pre-programmed with specific algorithms. Consumer drones typically use mobile device together with remote controllers to realize flight control and video transmission. Implementing different functions on mobile… More >

  • Open Access


    A Review on Deep Learning Approaches to Image Classification and Object Segmentation

    Hao Wu1, Qi Liu2, 3, *, Xiaodong Liu4

    CMC-Computers, Materials & Continua, Vol.60, No.2, pp. 575-597, 2019, DOI:10.32604/cmc.2019.03595

    Abstract Deep learning technology has brought great impetus to artificial intelligence, especially in the fields of image processing, pattern and object recognition in recent years. Present proposed artificial neural networks and optimization skills have effectively achieved large-scale deep learnt neural networks showing better performance with deeper depth and wider width of networks. With the efforts in the present deep learning approaches, factors, e.g., network structures, training methods and training data sets are playing critical roles in improving the performance of networks. In this paper, deep learning models in recent years are summarized and compared with detailed discussion of several typical networks… More >

  • Open Access


    Inverse Scatterer Reconstruction in a Halfplane Using Surficial SH Line Sources

    C. Jeong1, L.F. Kallivokas2

    CMES-Computer Modeling in Engineering & Sciences, Vol.35, No.1, pp. 49-72, 2008, DOI:10.3970/cmes.2008.035.049

    Abstract We discuss the inverse scattering problem of identifying the shape and location of a rigid scatterer fully buried in a homogeneous halfplane, when illuminated by surficial (line) wave sources generating SH waves. To this end, we consider the full-waveform response of the coupled host-obstacle system in the frequency domain, and employ the apparatus of partial-differential-equation-constrained optimization, augmented with total differentiation for tracking shape evolutions across inversion iterations, and specialized continuation schemes in lieu of formal regularization. We report numerical results that provide evidence of algorithmic robustness for detecting a variety of shapes, including elliptically- and kite-shaped obstacles. More >

Displaying 91-100 on page 10 of 101. Per Page