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  • Open Access


    Segmentation Based Real Time Anomaly Detection and Tracking Model for Pedestrian Walkways

    B. Sophia1,*, D. Chitra2

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2491-2504, 2023, DOI:10.32604/iasc.2023.029799

    Abstract Presently, video surveillance is commonly employed to ensure security in public places such as traffic signals, malls, railway stations, etc. A major challenge in video surveillance is the identification of anomalies that exist in it such as crimes, thefts, and so on. Besides, the anomaly detection in pedestrian walkways has gained significant attention among the computer vision communities to enhance pedestrian safety. The recent advances of Deep Learning (DL) models have received considerable attention in different processes such as object detection, image classification, etc. In this aspect, this article designs a new Panoptic Feature Pyramid Network based Anomaly Detection and… More >

  • Open Access


    Deep Learning Enabled Object Detection and Tracking Model for Big Data Environment

    K. Vijaya Kumar1, E. Laxmi Lydia2, Ashit Kumar Dutta3, Velmurugan Subbiah Parvathy4, Gobi Ramasamy5, Irina V. Pustokhina6,*, Denis A. Pustokhin7

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2541-2554, 2022, DOI:10.32604/cmc.2022.028570

    Abstract Recently, big data becomes evitable due to massive increase in the generation of data in real time application. Presently, object detection and tracking applications becomes popular among research communities and finds useful in different applications namely vehicle navigation, augmented reality, surveillance, etc. This paper introduces an effective deep learning based object tracker using Automated Image Annotation with Inception v2 based Faster RCNN (AIA-IFRCNN) model in big data environment. The AIA-IFRCNN model annotates the images by Discriminative Correlation Filter (DCF) with Channel and Spatial Reliability tracker (CSR), named DCF-CSRT model. The AIA-IFRCNN technique employs Faster RCNN for object detection and tracking,… More >

  • Open Access


    Robust Visual Tracking Models Designs Through Kernelized Correlation Filters

    Detian Huang1, Peiting Gu2, Hsuan-Ming Feng3,*, Yanming Lin1, Lixin Zheng1

    Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 313-322, 2020, DOI:10.31209/2019.100000105

    Abstract To tackle the problem of illumination sensitive, scale variation, and occlusion in the Kernelized Correlation Filters (KCF) tracker, an improved robust tracking algorithm based on KCF is proposed. Firstly, the color attribute was introduced to represent the target, and the dimension of target features was reduced adaptively to obtain low-dimensional and illumination-insensitive target features with the locally linear embedding approach. Secondly, an effective appearance model updating strategy is designed, and then the appearance model can be adaptively updated according to the Peak-to-Sidelobe Ratio value. Finally, the low-dimensional color features and the HOG features are utilized to determine the target state… More >

  • Open Access


    Pseudo-Linear Kalman Filter-based GPS Active Aircraft Tracking Algorithm

    Zhimin Chen1,2, Yuanxin Qu1, Yuming Bo1, Yongliang Zhang1, Bo Cong1, Xinfeng Yu1

    CMES-Computer Modeling in Engineering & Sciences, Vol.108, No.5, pp. 303-313, 2015, DOI:10.3970/cmes.2015.108.303

    Abstract For the sake of a higher accuracy of active aircraft GPS tracking, the tracking algorithm based on pseudo-linear Kalman filter is hereby proposed. This algorithm simplifies geometrical and algebraic relations to obtain a pseudo-linear model, then tracking the target by means of Kalman filter algorithm. Meanwhile, the tracking algorithm is studied to build a velocity & position tracking model and a velocity & acceleration tracking model. As shown by the experimental result, the tracking algorithm based on pseudo-linear Kalman filter can meet the requirement of active aircraft GPS tracking, but also attain a higher tracking accuracy in velocity & acceleration… More >

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