TY - GEN
T1 - Vehicle semantics extraction and retrieval for long-term carpark video surveillance
AU - Cheong, Clarence Weihan
AU - Lim, Ryan Woei-Sheng
AU - See, John
AU - Wong, Lai Kuan
AU - Tan, Ian K. T.
AU - Aris, Azrin
N1 - Funding Information:
Acknowledgment. This work is supported in part by Telekom Malaysia Research & Development Grant No. RDTC/160903 (SHERLOCK) and Multimedia University.
Publisher Copyright:
© Springer International Publishing AG 2018.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2018/1/13
Y1 - 2018/1/13
N2 - Car park video surveillance data provides plenty of semantic rich data such as vehicle color, trajectory, speed, and type which can be tapped into and extracted for video and data analytics. We present methods for extracting and retrieving color and motion semantics from long term carpark video surveillance. This is a challenging task in outdoor scenarios due to ever-changing illumination and weather conditions, while retrieval time also increases as data size grows. To address these challenges, we subdivided the search space into smaller chunks by introducing spatio-temporal cubes or atoms, which can store and retrieve these semantics at ease. The proposed method was tested on 2, days of continuous data from an outdoor carpark under various lighting and weather conditions. We report the precision, recall and F1 scores to determine the overall performance of the system.
AB - Car park video surveillance data provides plenty of semantic rich data such as vehicle color, trajectory, speed, and type which can be tapped into and extracted for video and data analytics. We present methods for extracting and retrieving color and motion semantics from long term carpark video surveillance. This is a challenging task in outdoor scenarios due to ever-changing illumination and weather conditions, while retrieval time also increases as data size grows. To address these challenges, we subdivided the search space into smaller chunks by introducing spatio-temporal cubes or atoms, which can store and retrieve these semantics at ease. The proposed method was tested on 2, days of continuous data from an outdoor carpark under various lighting and weather conditions. We report the precision, recall and F1 scores to determine the overall performance of the system.
KW - Carpark surveillance
KW - Retrieval systems
KW - Vehicle semantic extraction
UR - http://www.scopus.com/inward/record.url?scp=85042095990&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-73600-6_27
DO - 10.1007/978-3-319-73600-6_27
M3 - Conference contribution
AN - SCOPUS:85042095990
SN - 9783319735993
T3 - Lecture Notes in Computer Science
SP - 315
EP - 326
BT - MultiMedia Modeling. MMM 2018
PB - Springer
T2 - 24th International Conference on MultiMedia Modeling 2018
Y2 - 5 February 2018 through 7 February 2018
ER -