5,501 research outputs found
Semantic Background Subtraction
peer reviewedWe introduce the notion of semantic background subtraction, a novel framework for motion detection in video sequences. The key innovation consists to leverage object-level semantics to address the variety of challenging scenarios for background subtraction. Our framework combines the information of a semantic segmentation algorithm, expressed by a probability for each pixel, with the output of any background subtraction algorithm to reduce false positive detections produced by illumination changes, dynamic backgrounds, strong shadows, and ghosts. In addition, it maintains a fully semantic background model to improve the detection of camouflaged foreground objects. Experiments led on the CDNet dataset show that we managed to improve, significantly, almost all background subtraction algorithms of the CDNet leaderboard, and reduce the mean overall error rate of all the 34 algorithms (resp. of the best 5 algorithms) by roughly 50% (resp. 20%). Note that a C++ implementation of the framework is available at http://www.telecom.ulg.ac.be/semantic
An edge-based approach for robust foreground detection
Foreground segmentation is an essential task in many image processing applications and a commonly used approach to obtain foreground objects from the background. Many techniques exist, but due to shadows and changes in illumination the segmentation of foreground objects from the background remains challenging. In this paper, we present a powerful framework for detections of moving objects in real-time video processing applications under various lighting changes. The novel approach is based on a combination of edge detection and recursive smoothing techniques.We use edge dependencies as statistical features of foreground and background regions and define the foreground as regions containing moving edges. The background is described by short- and long-term estimates. Experiments prove the robustness of our method in the presence of lighting changes in sequences compared to other widely used background subtraction techniques
Background Subtraction with Real-time Semantic Segmentation
Accurate and fast foreground object extraction is very important for object
tracking and recognition in video surveillance. Although many background
subtraction (BGS) methods have been proposed in the recent past, it is still
regarded as a tough problem due to the variety of challenging situations that
occur in real-world scenarios. In this paper, we explore this problem from a
new perspective and propose a novel background subtraction framework with
real-time semantic segmentation (RTSS). Our proposed framework consists of two
components, a traditional BGS segmenter and a real-time semantic
segmenter . The BGS segmenter aims to construct
background models and segments foreground objects. The real-time semantic
segmenter is used to refine the foreground segmentation outputs
as feedbacks for improving the model updating accuracy. and
work in parallel on two threads. For each input frame , the
BGS segmenter computes a preliminary foreground/background
(FG/BG) mask . At the same time, the real-time semantic segmenter
extracts the object-level semantics . Then, some specific
rules are applied on and to generate the final detection
. Finally, the refined FG/BG mask is fed back to update the
background model. Comprehensive experiments evaluated on the CDnet 2014 dataset
demonstrate that our proposed method achieves state-of-the-art performance
among all unsupervised background subtraction methods while operating at
real-time, and even performs better than some deep learning based supervised
algorithms. In addition, our proposed framework is very flexible and has the
potential for generalization
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
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