2,897 research outputs found
Total Variation Regularized Tensor RPCA for Background Subtraction from Compressive Measurements
Background subtraction has been a fundamental and widely studied task in
video analysis, with a wide range of applications in video surveillance,
teleconferencing and 3D modeling. Recently, motivated by compressive imaging,
background subtraction from compressive measurements (BSCM) is becoming an
active research task in video surveillance. In this paper, we propose a novel
tensor-based robust PCA (TenRPCA) approach for BSCM by decomposing video frames
into backgrounds with spatial-temporal correlations and foregrounds with
spatio-temporal continuity in a tensor framework. In this approach, we use 3D
total variation (TV) to enhance the spatio-temporal continuity of foregrounds,
and Tucker decomposition to model the spatio-temporal correlations of video
background. Based on this idea, we design a basic tensor RPCA model over the
video frames, dubbed as the holistic TenRPCA model (H-TenRPCA). To characterize
the correlations among the groups of similar 3D patches of video background, we
further design a patch-group-based tensor RPCA model (PG-TenRPCA) by joint
tensor Tucker decompositions of 3D patch groups for modeling the video
background. Efficient algorithms using alternating direction method of
multipliers (ADMM) are developed to solve the proposed models. Extensive
experiments on simulated and real-world videos demonstrate the superiority of
the proposed approaches over the existing state-of-the-art approaches.Comment: To appear in IEEE TI
CVABS: Moving Object Segmentation with Common Vector Approach for Videos
Background modelling is a fundamental step for several real-time computer
vision applications that requires security systems and monitoring. An accurate
background model helps detecting activity of moving objects in the video. In
this work, we have developed a new subspace based background modelling
algorithm using the concept of Common Vector Approach with Gram-Schmidt
orthogonalization. Once the background model that involves the common
characteristic of different views corresponding to the same scene is acquired,
a smart foreground detection and background updating procedure is applied based
on dynamic control parameters. A variety of experiments is conducted on
different problem types related to dynamic backgrounds. Several types of
metrics are utilized as objective measures and the obtained visual results are
judged subjectively. It was observed that the proposed method stands
successfully for all problem types reported on CDNet2014 dataset by updating
the background frames with a self-learning feedback mechanism.Comment: 12 Pages, 4 Figures, 1 Tabl
A spatially distributed model for foreground segmentation
Foreground segmentation is a fundamental first processing stage for vision systems which monitor real-world activity. In this paper we consider the problem of achieving robust segmentation in scenes where the appearance of the background varies unpredictably over time. Variations may be caused by processes such as moving water, or foliage moved by wind, and typically degrade the performance of standard per-pixel background models.
Our proposed approach addresses this problem by modeling homogeneous regions of scene pixels as an adaptive mixture of Gaussians in color and space. Model components are used to represent both the scene background and moving foreground objects. Newly observed pixel values are probabilistically classified, such that the spatial variance of the model components supports correct classification even when the background appearance is significantly distorted. We evaluate our method over several challenging video sequences, and compare our results with both per-pixel and Markov Random Field based models. Our results show the effectiveness of our approach in reducing incorrect classifications
Background Subtraction Methods in Video Streams: A Review
Background subtraction is one of the most important parts in image and video processing field. There are some unnecessary parts during the image or video processing, and should be removed, because they lead to more execution time or required memory. Several subtraction methods have been presented for the time being, but find the best-suited method is an issue, which this study is going to address. Furthermore, each process needs to the specific subtraction technique, and knowing this issue helps researchers to achieve faster and higher performance in their research. This paper presents a comparative study of several existing background subtraction methods which have been investigated from simple background subtraction to more complex statistical techniques. The goal of this study is to provide a view of the strengths and drawbacks of the widely used methods. The methods are compared based on their memory requirement, the computational time and their robustness of different videos. Finally, a comparison between the existing methods has been employed with some factors like computational time or memory requirements. It is also hoped that this analysis helps researchers to address the difficulty of selecting the most convenient method for background subtraction
Randomize to Generalize: Domain Randomization for Runway FOD Detection
Tiny Object Detection is challenging due to small size, low resolution,
occlusion, background clutter, lighting conditions and small object-to-image
ratio. Further, object detection methodologies often make underlying assumption
that both training and testing data remain congruent. However, this presumption
often leads to decline in performance when model is applied to
out-of-domain(unseen) data. Techniques like synthetic image generation are
employed to improve model performance by leveraging variations in input data.
Such an approach typically presumes access to 3D-rendered datasets. In
contrast, we propose a novel two-stage methodology Synthetic Randomized Image
Augmentation (SRIA), carefully devised to enhance generalization capabilities
of models encountering 2D datasets, particularly with lower resolution which is
more practical in real-world scenarios. The first stage employs a weakly
supervised technique to generate pixel-level segmentation masks. Subsequently,
the second stage generates a batch-wise synthesis of artificial images,
carefully designed with an array of diverse augmentations. The efficacy of
proposed technique is illustrated on challenging foreign object debris (FOD)
detection. We compare our results with several SOTA models including CenterNet,
SSD, YOLOv3, YOLOv4, YOLOv5, and Outer Vit on a publicly available FOD-A
dataset. We also construct an out-of-distribution test set encompassing 800
annotated images featuring a corpus of ten common categories. Notably, by
harnessing merely 1.81% of objects from source training data and amalgamating
with 29 runway background images, we generate 2227 synthetic images. Subsequent
model retraining via transfer learning, utilizing enriched dataset generated by
domain randomization, demonstrates significant improvement in detection
accuracy. We report that detection accuracy improved from an initial 41% to 92%
for OOD test set.Comment: 29 pages, 9 figure
Are object detection assessment criteria ready for maritime computer vision?
Maritime vessels equipped with visible and infrared cameras can complement
other conventional sensors for object detection. However, application of
computer vision techniques in maritime domain received attention only recently.
The maritime environment offers its own unique requirements and challenges.
Assessment of the quality of detections is a fundamental need in computer
vision. However, the conventional assessment metrics suitable for usual object
detection are deficient in the maritime setting. Thus, a large body of related
work in computer vision appears inapplicable to the maritime setting at the
first sight. We discuss the problem of defining assessment metrics suitable for
maritime computer vision. We consider new bottom edge proximity metrics as
assessment metrics for maritime computer vision. These metrics indicate that
existing computer vision approaches are indeed promising for maritime computer
vision and can play a foundational role in the emerging field of maritime
computer vision
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