Purpose
The purpose of this paper is to critically analyze the state-of-the-art session identification techniques used in web usage mining (WUM) process in terms of their limitations, features, and methodologies.
Design/methodology/approach
In this research, systematic literature review has been conducted using review protocol approach. The methodology consisted of a comprehensive search for relevant literature over the period of 2005-2015, using four online database repositories (i.e. IEEE, Springer, ACM Digital Library, and ScienceDirect).
Findings
The findings revealed that this research area is still immature and existing literature lacks the critical review of recent session identification techniques used in WUM process.
Originality/value
The contribution of this study is to provide a structured overview of the research developments, to critically review the existing session identification techniques, highlight their limitations and associated challenges and identify areas where further improvements are required so as to complement the performance of existing techniques.
Abstract-Pedestrian detection is an important aspect of autonomous vehicle driving as recognizing pedestrians helps in reducing accidents between the vehicles and the pedestrians. In literature, feature based approaches have been mostly used for pedestrian detection. Features from different body portions are extracted and analyzed for interpreting the presence or absence of a person in a particular region in front of car. But these approaches alone are not enough to differentiate humans from non-humans in dynamic environments, where background is continuously changing. We present an automated pedestrian detection system by finding pedestrians' motion patterns and combing them with HOG features. The proposed scheme achieved 17.7% and 14.22% average miss rate on ETHZ and Caltech datasets, respectively.
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