부산대학교 도서관

Human and object detection is a continuously explored subject in tracking and navigation applications as well as within the machine vision community. More precisely, in navigation applications that are designed for robotics purposes or in order to support car drivers, the real-time detection of presence of humans and other objects is an important and challenging task. Specifically, human and object detection is a significant part of a human-computer collaboration in the sense of allowing automatic navigation and control systems to obtain a better model of the real world. Thus, new concepts are needed that will overcome difficulties faced by existing human detection approaches and will allow navigation systems to perform accurately. This paper offers a general classification scheme combining different detector systems towards navigation tasks. To evaluate the performance of the proposed methodology we used INRIA and PASCAL VOC 2007 datasets. Experimental results show that the combination of different image feature descriptors, classifier models and deep learning techniques are advantageous for human detection.