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RETRACTED ARTICLE: An effiecient human tracking system using Haar-like and hog feature extraction

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This article was retracted on 05 December 2022

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Abstract

The traditional human tracking systems are often prone to problems which can be deduced either from the subject, camera or background movements which includes major changes in posture, appearance, clothing and lighting of the background. The work introduced here proposes a system for extracting and tracking objects from a video sequence by initializing the process of feature extraction of the selected area of the human. The need for cascade extraction by using Haar-like features, is to basically decrease the use of crude or raw pixel values and then make classification easier. The major issue here is the problem in extracting the required finite set of input for identifying the characteristics necessary for domain encoding rule In the proposed work the major aim is to address the tracking procedure by facilitating the exclusive histogram of oriented gradient (HOG) methodology, using which only the human’s unit element is embraced in the usual tracking system, making it problematic for robust monitoring. The features of Haar-like and HOG methods are combined to propose a tracking system which uses the Haar characteristics for the object’s structure and the HOG features for the edge. A set of mixed features is developed with these two features. Boosting Online’s selection feature is used to select important features and update these online features to understand the optimal choice using cascading SVM classifier.

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Authors and Affiliations

  1. Reva University, Bengaluru, India

    Dinesh Prasanna

  2. School of Computing and IT, Reva University, Bengaluru, India

    M. Prabhakar

Authors
  1. Dinesh Prasanna
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  2. M. Prabhakar
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Correspondence to Dinesh Prasanna.

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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s10586-022-03897-5

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Prasanna, D., Prabhakar, M. RETRACTED ARTICLE: An effiecient human tracking system using Haar-like and hog feature extraction. Cluster Comput 22 (Suppl 2), 2993–3000 (2019). https://doi.org/10.1007/s10586-018-1747-5

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  • DOI: https://doi.org/10.1007/s10586-018-1747-5

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