Labeled dataset for integral evaluation of moving object detection algorithms: LASIESTA

•Complete database for assessing the quality of foreground detection strategies.•Indoor and outdoor sequences with many categories addressing different challenges.•All the sequences are fully annotated at both pixel and object levels.•Information concerning stationary foreground objects.•Sequences r...

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Vydáno v:Computer vision and image understanding Ročník 152; s. 103 - 117
Hlavní autoři: Cuevas, Carlos, Yáñez, Eva María, García, Narciso
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Inc 01.11.2016
Témata:
Background subtraction
Challenges
Dataset
Foreground detection
Ground-truth
Moving camera
Moving object detection
Stationary foreground
Background subtraction
Dataset
Challenges
Foreground detection
Database
Ground-truth
Moving camera
Moving object detection
Stationary foreground
ISSN:1077-3142, 1090-235X
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Popis
Shrnutí:•Complete database for assessing the quality of foreground detection strategies.•Indoor and outdoor sequences with many categories addressing different challenges.•All the sequences are fully annotated at both pixel and object levels.•Information concerning stationary foreground objects.•Sequences recorded with static and moving cameras. [Display omitted] A public, complete, compact, and well structured database is proposed, which allows to test moving object detection strategies. The database is composed of many real indoor and outdoor sequences organized in different categories, each of one covering a specific challenge. In contrast to other databases, the proposed one is fully annotated at both pixel and object levels. Therefore, it is suitable for strategies exclusively focused on the detection of moving objects and also for those that integrate tracking algorithms in their detection approaches. Additionally, it contains sequences recorded with static and moving cameras and it also provides information about the moving objects remaining temporally static. To test its usefulness, the database has been used to assess the quality of some outstanding moving object detection methods.
ISSN:1077-3142
1090-235X
DOI:10.1016/j.cviu.2016.08.005