Naturalistic driving data : managing and working with large databases for road and traffic management research

Paper presented at the 33rd Annual Southern African Transport Conference 7-10 July 2014 "Leading Transport into the Future", CSIR International Convention Centre, Pretoria, South Africa.Naturalistic driving and field operational tests are used worldwide to collect data from drivers in order to better understand the human, vehicle and environment interactions. The fairly new methodology has already provided great insight into numerous driver behaviours that could previously not be observed directly. The data is collected with a data acquisition system which is installed in the vehicle. This system consists of cameras facing the driver (and passengers) as well as cameras facing outward. An on-board computer is installed in the vehicle and collects information about the vehicle. This information includes satellite positions, data and time as well as speed and acceleration and deceleration data. The system collects large volumes of data and the challenge is to manage this data efficiently as currently the datasets take-up much storage space, are in different formats necessitating that different software programs be used to download, transcribe and analyse the data. This paper provides an overview of the challenges experienced while working with these large data sets as well as some of the possible solutions identified. The findings and recommendations from this study should prove useful to other researchers and practitioners interested in working with naturalistic data.This paper was transferred from the original CD ROM created for this conference. The material was published using Adobe Acrobat 10.1.0 Technology. The original CD ROM was produced by CE Projects cc. Postal Address: PO Box 560 Irene 0062 South Africa. Tel.: +27 12 667 2074 Fax: +27 12 667 2766 E-mail: [email protected]

A rapid and robust method for shot boundary detection and classification in uncompressed MPEG video sequences

Abstract Shot boundary and classification is the first and most important step for further analysis of video content. Shot transitions include abrupt changes and gradual changes. A rapid and robust method for shot boundary detection and classification in MPEG compressed sequences is proposed in this paper. We firstly only decode I frames partly in video sequences to generate DC images and then calculate the difference values of histogram of these DC images in order to detect roughly the shot boundary. Then, for abrupt change detection, shot boundary is precisely located by movement information of B frames. Shot gradual change is located by difference values of successive N I frames and classified by the alteration of the number of intra coding macroblocks (MBs) in P frames. All features such as the number of MBs in frames are extracted from uncompressed video sequences. Experiments have been done on the standard TRECVid video database and others to reveal the performance of the proposed method