TY - GEN
T1 - Fast object instance search in videos from one example
AU - Meng, Jingjing
AU - Yuan, Junsong
AU - Tan, Yap Peng
AU - Wang, Gang
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/12/9
Y1 - 2015/12/9
N2 - We present an efficient approach to search for and locate all occurrences of a specific object in large video volumes, given a single query example. Locations of object occurrences are returned as spatio-temporal trajectories in the 3D video volume. Despite much work on object instance search in image datasets, these methods locate the object independently in each image, therefore do not preserve the spatio-temporal consistency in consecutive video frames. This results in sub-optimal performance if directly applied to videos, as will be shown in our experiments. We propose to locate the object jointly across video frames using spatio-temporal search. The efficiency and effectiveness of the proposed approach is demonstrated on a consumer video dataset consisting of crawled YouTube videos and mobile captured consumer clips. Our method significantly improves the localized search accuracy over the baseline, which treats each frame independently. Moreover, it is able to find the top 100 object trajectories in the 5.5-hour dataset within 30 seconds.
AB - We present an efficient approach to search for and locate all occurrences of a specific object in large video volumes, given a single query example. Locations of object occurrences are returned as spatio-temporal trajectories in the 3D video volume. Despite much work on object instance search in image datasets, these methods locate the object independently in each image, therefore do not preserve the spatio-temporal consistency in consecutive video frames. This results in sub-optimal performance if directly applied to videos, as will be shown in our experiments. We propose to locate the object jointly across video frames using spatio-temporal search. The efficiency and effectiveness of the proposed approach is demonstrated on a consumer video dataset consisting of crawled YouTube videos and mobile captured consumer clips. Our method significantly improves the localized search accuracy over the baseline, which treats each frame independently. Moreover, it is able to find the top 100 object trajectories in the 5.5-hour dataset within 30 seconds.
KW - object instance search in videos
KW - spatio-temporal trajectory
UR - https://www.scopus.com/pages/publications/84956617734
U2 - 10.1109/ICIP.2015.7351634
DO - 10.1109/ICIP.2015.7351634
M3 - Conference contribution
AN - SCOPUS:84956617734
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 4381
EP - 4385
BT - 2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings
PB - IEEE Computer Society
T2 - IEEE International Conference on Image Processing, ICIP 2015
Y2 - 27 September 2015 through 30 September 2015
ER -