Next Article in Journal
Development and Control of a Pneumatic-Actuator 3-DOF Translational Parallel Manipulator with Robot Vision
Next Article in Special Issue
Depth Map Upsampling via Multi-Modal Generative Adversarial Network
Previous Article in Journal
Guide Star Selection for the Three-FOV Daytime Star Sensor
Previous Article in Special Issue
Baseball Player Behavior Classification System Using Long Short-Term Memory with Multimodal Features
Article Menu
Issue 6 (March-2) cover image

Export Article

Open AccessArticle

Combining Weighted Contour Templates with HOGs for Human Detection Using Biased Boosting

Department of Computer and Communication Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
Department of Electrical Engineering, National University of Kaohsiung, Kaohsiung 80811, Taiwan
Author to whom correspondence should be addressed.
Sensors 2019, 19(6), 1458;
Received: 14 February 2019 / Revised: 21 March 2019 / Accepted: 21 March 2019 / Published: 25 March 2019
(This article belongs to the Special Issue Selected Papers from INNOV 2018)
PDF [4224 KB, uploaded 28 March 2019]


This paper proposes a method to detect humans in the image that is an important issue for many applications, such as video surveillance in smart home and driving assistance systems. A kind of local feature called the histogram of oriented gradients (HOGs) has been widely used in describing the human appearance and its effectiveness has been proven in the literature. A learning framework called boosting is adopted to select a set of classifiers based on HOGs for human detection. However, in the case of a complex background or noise effect, the use of HOGs results in the problem of false detection. To alleviate this, the proposed method imposes a classifier based on weighted contour templates to the boosting framework. The way to combine the global contour templates with local HOGs is by adjusting the bias of a support vector machine (SVM) for the local classifier. The method proposed for feature combination is referred to as biased boosting. For covering the human appearance in various poses, an expectation maximization algorithm is used which is a kind of iterative algorithm is used to construct a set of representative weighted contour templates instead of manual annotation. The encoding of different weights to the contour points gives the templates more discriminative power in matching. The experiments provided exhibit the superiority of the proposed method in detection accuracy. View Full-Text
Keywords: HOGs; global contour template; expectation maximization; boosting HOGs; global contour template; expectation maximization; boosting

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Huang, S.-S.; Ku, S.-H.; Hsiao, P.-Y. Combining Weighted Contour Templates with HOGs for Human Detection Using Biased Boosting. Sensors 2019, 19, 1458.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top