Learning Spatial-Temporal Regularized Tensor Sparse RPCA for Background Subtraction

• URL: http://arxiv.org/abs/2309.15576v1
• Date: Wed, 27 Sep 2023 11:21:31 GMT
• Title: Learning Spatial-Temporal Regularized Tensor Sparse RPCA for Background Subtraction
• Authors: Basit Alawode and Sajid Javed
• Abstract summary: We present a spatial-temporal regularized tensor sparse RPCA algorithm for precise background subtraction. Experiments are performed on six publicly available background subtraction datasets.
• Score: 6.825970634402847
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Video background subtraction is one of the fundamental problems in computer vision that aims to segment all moving objects. Robust principal component analysis has been identified as a promising unsupervised paradigm for background subtraction tasks in the last decade thanks to its competitive performance in a number of benchmark datasets. Tensor robust principal component analysis variations have improved background subtraction performance further. However, because moving object pixels in the sparse component are treated independently and do not have to adhere to spatial-temporal structured-sparsity constraints, performance is reduced for sequences with dynamic backgrounds, camouflaged, and camera jitter problems. In this work, we present a spatial-temporal regularized tensor sparse RPCA algorithm for precise background subtraction. Within the sparse component, we impose spatial-temporal regularizations in the form of normalized graph-Laplacian matrices. To do this, we build two graphs, one across the input tensor spatial locations and the other across its frontal slices in the time domain. While maximizing the objective function, we compel the tensor sparse component to serve as the spatiotemporal eigenvectors of the graph-Laplacian matrices. The disconnected moving object pixels in the sparse component are preserved by the proposed graph-based regularizations since they both comprise of spatiotemporal subspace-based structure. Additionally, we propose a unique objective function that employs batch and online-based optimization methods to jointly maximize the background-foreground and spatial-temporal regularization components. Experiments are performed on six publicly available background subtraction datasets that demonstrate the superior performance of the proposed algorithm compared to several existing methods. Our source code will be available very soon.

Related papers

• DSLO: Deep Sequence LiDAR Odometry Based on Inconsistent Spatio-temporal Propagation [66.8732965660931]
  paper introduces a 3D point cloud sequence learning model based on inconsistent-temporal propagation for LiDAR odometry DSLO. It consists of a pyramid structure with a sequential pose module, a hierarchical pose refinement module, and a temporal feature propagation module.
  arXiv  Detail & Related papers  (2024-09-01T15:12:48Z)
• Betrayed by Attention: A Simple yet Effective Approach for Self-supervised Video Object Segmentation [76.68301884987348]
  We propose a simple yet effective approach for self-supervised video object segmentation (VOS) Our key insight is that the inherent structural dependencies present in DINO-pretrained Transformers can be leveraged to establish robust-temporal segmentation correspondences in videos. Our method demonstrates state-of-the-art performance across multiple unsupervised VOS benchmarks and excels in complex real-world multi-object video segmentation tasks.
  arXiv  Detail & Related papers  (2023-11-29T18:47:17Z)
• Hyperspectral Target Detection Based on Low-Rank Background Subspace Learning and Graph Laplacian Regularization [2.9626402880497267]
  Hyperspectral target detection is good at finding dim and small objects based on spectral characteristics. Existing representation-based methods are hindered by the problem of the unknown background dictionary. This paper proposes an efficient optimizing approach based on low-rank representation (LRR) and graph Laplacian regularization (GLR)
  arXiv  Detail & Related papers  (2023-06-01T13:51:08Z)
• Learning a Fast 3D Spectral Approach to Object Segmentation and Tracking over Space and Time [21.130594354306815]
  We pose video object segmentation as spectral graph clustering in space and time. We introduce a novel and efficient method based on 3D filtering for approximating the spectral solution. We extend the formulation of our approach beyond the segmentation task, into the realm of object tracking.
  arXiv  Detail & Related papers  (2022-12-15T18:59:07Z)
• Spatial-temporal traffic modeling with a fusion graph reconstructed by tensor decomposition [10.104097475236014]
  Graph convolutional networks (GCNs) have been widely used in traffic flow prediction. The design of the spatial-temporal graph adjacency matrix is a key to the success of GCNs. This paper proposes reconstructing the binary adjacency matrix via tensor decomposition.
  arXiv  Detail & Related papers  (2022-12-12T01:44:52Z)
• Spatial-Temporal Adaptive Graph Convolution with Attention Network for Traffic Forecasting [4.1700160312787125]
  We propose a novel network, Spatial-Temporal Adaptive graph convolution with Attention Network (STAAN) for traffic forecasting. Firstly, we adopt an adaptive dependency matrix instead of using a pre-defined matrix during GCN processing to infer the inter-dependencies among nodes. Secondly, we integrate PW-attention based on graph attention network which is designed for global dependency, and GCN as spatial block.
  arXiv  Detail & Related papers  (2022-06-07T09:08:35Z)
• Spatiotemporal Graph Neural Network based Mask Reconstruction for Video Object Segmentation [70.97625552643493]
  This paper addresses the task of segmenting class-agnostic objects in semi-supervised setting. We propose a novel graph neuralS network (TG-Net) which captures the local contexts by utilizing all proposals.
  arXiv  Detail & Related papers  (2020-12-10T07:57:44Z)
• DS-Net: Dynamic Spatiotemporal Network for Video Salient Object Detection [78.04869214450963]
  We propose a novel dynamic temporal-temporal network (DSNet) for more effective fusion of temporal and spatial information. We show that the proposed method achieves superior performance than state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2020-12-09T06:42:30Z)
• Joint Spatial-Temporal Optimization for Stereo 3D Object Tracking [34.40019455462043]
  We propose a joint spatial-temporal optimization-based stereo 3D object tracking method. From the network, we detect corresponding 2D bounding boxes on adjacent images and regress an initial 3D bounding box. Dense object cues that associating to the object centroid are then predicted using a region-based network.
  arXiv  Detail & Related papers  (2020-04-20T13:59:46Z)
• Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition [79.33539539956186]
  We propose a simple method to disentangle multi-scale graph convolutions and a unified spatial-temporal graph convolutional operator named G3D. By coupling these proposals, we develop a powerful feature extractor named MS-G3D based on which our model outperforms previous state-of-the-art methods on three large-scale datasets.
  arXiv  Detail & Related papers  (2020-03-31T11:28:25Z)
• Spatial Pyramid Based Graph Reasoning for Semantic Segmentation [67.47159595239798]
  We apply graph convolution into the semantic segmentation task and propose an improved Laplacian. The graph reasoning is directly performed in the original feature space organized as a spatial pyramid. We achieve comparable performance with advantages in computational and memory overhead.
  arXiv  Detail & Related papers  (2020-03-23T12:28:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.