Geometrical-based approach for robust human image detection

Al-Hazaimeh, OM; Al-Nawashi, M; Saraee, MH

Abstract

In recent years, object detection and classification has been gaining more attention, thus, there are several human object detection algorithms being used to locate and recognize human objects in images. The research of image processing and analyzing based on human shape is one of the hot topic due to the wide applicability in real applications. In this paper, we present a new object classification approach. The new approach will use a simple and robust geometrical model to classify the detected object as human or non-human in the images. In the proposed approach, the object is detected. Then the detected object under different conditions can be accurately classified (i.e. human, non-human) by combining the features that are extracted from the upper portion of the contour and the proposed geometrical model parameters. A software-based simulation using Matlab was performed using INRIA dataset and the obtained results are validated by comparing with five state-of-art approaches in literature and some of the machine learning approaches such as artificial neural networks (ANN), support vector machine (SVM), and random forest (RF). The experimental results show that the proposed object classification approach is efficient and achieved a comparable accuracy to other machine learning approaches and other state-of-art approaches.
Keywords: Human classification, Geometrical model, INRIA, Machine learning, SVM, ANN, Random forest.

Citation

Al-Hazaimeh, O., Al-Nawashi, M., & Saraee, M. (2019). Geometrical-based approach for robust human image detection. Multimedia Tools and Applications, 78(6), 7029-7053. https://doi.org/10.1007/s11042-018-6401-y

• Journal Article Type: Article
• Acceptance Date: Jul 11, 2018
• Online Publication Date: Aug 4, 2018
• Publication Date: Mar 1, 2019
• Deposit Date: Jul 27, 2018
• Publicly Available Date: Aug 4, 2019
• Journal: Multimedia Tools and Applications
• Print ISSN: 1380-7501
• Electronic ISSN: 1573-7721
• Publisher: Springer Verlag
• Volume: 78
• Issue: 6
• Pages: 7029-7053
• DOI: https://doi.org/10.1007/s11042-018-6401-y
• Publisher URL: https://doi.org/10.1007/s11042-018-6401-y
• Related Public URLs: https://link.springer.com/journal/11042
• Additional Information: Access Information : This is a post-peer-review, pre-copyedit version of an article published in Multimedia Tools and Applications. The final authenticated version is available online at: http://dx.doi.org/10.1007/s11042-018-6401-y