2 research outputs found
The Fishnet Open Images Database: A Dataset for Fish Detection and Fine-Grained Categorization in Fisheries
Camera-based electronic monitoring (EM) systems are increasingly being
deployed onboard commercial fishing vessels to collect essential data for
fisheries management and regulation. These systems generate large quantities of
video data which must be reviewed on land by human experts. Computer vision can
assist this process by automatically detecting and classifying fish species,
however the lack of existing public data in this domain has hindered progress.
To address this, we present the Fishnet Open Images Database, a large dataset
of EM imagery for fish detection and fine-grained categorization onboard
commercial fishing vessels. The dataset consists of 86,029 images containing 34
object classes, making it the largest and most diverse public dataset of
fisheries EM imagery to-date. It includes many of the characteristic challenges
of EM data: visual similarity between species, skewed class distributions,
harsh weather conditions, and chaotic crew activity. We evaluate the
performance of existing detection and classification algorithms and demonstrate
that the dataset can serve as a challenging benchmark for development of
computer vision algorithms in fisheries. The dataset is available at
https://www.fishnet.ai/.Comment: In 8th Workshop on Fine-Grained Visual Categorization at CVPR 202
Background Subtraction in Real Applications: Challenges, Current Models and Future Directions
Computer vision applications based on videos often require the detection of
moving objects in their first step. Background subtraction is then applied in
order to separate the background and the foreground. In literature, background
subtraction is surely among the most investigated field in computer vision
providing a big amount of publications. Most of them concern the application of
mathematical and machine learning models to be more robust to the challenges
met in videos. However, the ultimate goal is that the background subtraction
methods developed in research could be employed in real applications like
traffic surveillance. But looking at the literature, we can remark that there
is often a gap between the current methods used in real applications and the
current methods in fundamental research. In addition, the videos evaluated in
large-scale datasets are not exhaustive in the way that they only covered a
part of the complete spectrum of the challenges met in real applications. In
this context, we attempt to provide the most exhaustive survey as possible on
real applications that used background subtraction in order to identify the
real challenges met in practice, the current used background models and to
provide future directions. Thus, challenges are investigated in terms of
camera, foreground objects and environments. In addition, we identify the
background models that are effectively used in these applications in order to
find potential usable recent background models in terms of robustness, time and
memory requirements.Comment: Submitted to Computer Science Revie