Authors:
Priyam Bakliwal
1
;
Guruprasad M. Hegde
2
and
C. V. Jawahar
1
Affiliations:
1
International Institute of Information Technology, India
;
2
Bosch Research and Technology Centre, India
Keyword(s):
Video-processing, Active-leaning, Surveillance Video Annotations, Tracking.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Motion, Tracking and Stereo Vision
;
Tracking and Visual Navigation
;
Video Surveillance and Event Detection
Abstract:
We propose an active learning based solution for efficient, scalable and accurate annotations of objects in
video sequences. Recent computer vision solutions use machine learning. Effectiveness of these solutions
relies on the amount of available annotated data which again depends on the generation of huge amount of
accurately annotated data. In this paper, we focus on reducing the human annotation efforts with simultaneous
increase in tracking accuracy to get precise, tight bounding boxes around an object of interest. We use a novel
combination of two different tracking algorithms to track an object in the whole video sequence. We propose
a sampling strategy to sample the most informative frame which is given for human annotation. This newly
annotated frame is used to update the previous annotations. Thus, by collaborative efforts of both human and
the system we obtain accurate annotations with minimal effort. Using the proposed method, user efforts can
be reduced to half without co
mpromising on the annotation accuracy. We have quantitatively and qualitatively
validated the results on eight different datasets.
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