TY - JOUR
T1 - Personal driving diary
T2 - Automated recognition of driving events from first-person videos
AU - Ryoo, M. S.
AU - Choi, Sunglok
AU - Joung, Ji Hoon
AU - Lee, Jae Yeong
AU - Yu, Wonpil
PY - 2013
Y1 - 2013
N2 - In this paper, we introduce the concept of personal driving diary. A personal driving diary is a multimedia archive of a person's daily driving experience, describing important driving events of the user with annotated videos. This paper presents an automated system that constructs such multimedia diary by analyzing videos obtained from a vehicle-mounted camera. The proposed system recognizes important interactions between the driving vehicle and the other actors in videos (e.g., accident, overtaking, etc.), and labels them together with its contextual knowledge on the vehicle (e.g., mean velocity) to construct an event log. A decision tree based activity recognizer is designed, detecting driving events of vehicles and pedestrians from the first-person view videos by analyzing their trajectories and spatio-temporal relationships. The constructed diary enables efficient searching and event-based browsing of video clips, which helps the users when retrieving videos of dangerous situations. Our experiment confirms that the proposed system reliably generates driving diaries by annotating the vehicle events learned from training examples.
AB - In this paper, we introduce the concept of personal driving diary. A personal driving diary is a multimedia archive of a person's daily driving experience, describing important driving events of the user with annotated videos. This paper presents an automated system that constructs such multimedia diary by analyzing videos obtained from a vehicle-mounted camera. The proposed system recognizes important interactions between the driving vehicle and the other actors in videos (e.g., accident, overtaking, etc.), and labels them together with its contextual knowledge on the vehicle (e.g., mean velocity) to construct an event log. A decision tree based activity recognizer is designed, detecting driving events of vehicles and pedestrians from the first-person view videos by analyzing their trajectories and spatio-temporal relationships. The constructed diary enables efficient searching and event-based browsing of video clips, which helps the users when retrieving videos of dangerous situations. Our experiment confirms that the proposed system reliably generates driving diaries by annotating the vehicle events learned from training examples.
KW - Driving activity recognition
KW - First-person event detection
KW - Lifelogging
KW - Personal driving diary
UR - https://www.scopus.com/pages/publications/84885385765
U2 - 10.1016/j.cviu.2013.01.004
DO - 10.1016/j.cviu.2013.01.004
M3 - Article
AN - SCOPUS:84885385765
SN - 1077-3142
VL - 117
SP - 1299
EP - 1312
JO - Computer Vision and Image Understanding
JF - Computer Vision and Image Understanding
IS - 10
ER -