Surveillance video coding via low-rank and sparse decomposition

Chen, Chongyu; Cai, Jianfei; Wang, Lin; Shi, Guangming

doi:10.1145/2393347.2396294

Cited by 27 publications

(29 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For these characteristics, surveillance video compression methods can be divided into LRSD (low-rank sparse decomposition)-based and background modeling methods. LRSD-based methods [24][25][26] employ LRSD to decompose the input video into low-rank components representing the background and sparse components representing the moving objects, which are encoded by different methods. Background modeling methods [7,[27][28][29][30] use background modeling technology to build background frames for reference that improve the compression efficiency by improving the prediction accuracy.…”

Section: Video Compression Of Surveillance Videosmentioning

confidence: 99%

Towards Real-Time Service from Remote Sensing: Compression of Earth Observatory Video Data via Long-Term Background Referencing

Xiao

Zhu

et al. 2018

Remote Sensing

View full text Add to dashboard Cite

City surveillance enables many innovative applications of smart cities. However, the real-time utilization of remotely sensed surveillance data via unmanned aerial vehicles (UAVs) or video satellites is hindered by the considerable gap between the high data collection rate and the limited transmission bandwidth. High efficiency compression of the data is in high demand. Long-term background redundancy (LBR) (in contrast to local spatial/temporal redundancies in a single video clip) is a new form of redundancy common in Earth observatory video data (EOVD). LBR is induced by the repetition of static landscapes across multiple video clips and becomes significant as the number of video clips shot of the same area increases. Eliminating LBR improves EOVD coding efficiency considerably. First, this study proposes eliminating LBR by creating a long-term background referencing library (LBRL) containing high-definition geographically registered images of an entire area. Then, it analyzes the factors affecting the variations in the image representations of the background. Next, it proposes a method of generating references for encoding current video and develops the encoding and decoding framework for EOVD compression. Experimental results show that encoding UAV video clips with the proposed method saved an average of more than 54% bits using references generated under the same conditions. Bitrate savings reached 25-35% when applied to satellite video data with arbitrarily collected reference images. Applying the proposed coding method to EOVD will facilitate remote surveillance, which can foster the development of online smart city applications.

show abstract

Section: Video Compression Of Surveillance Videosmentioning

confidence: 99%

Towards Real-Time Service from Remote Sensing: Compression of Earth Observatory Video Data via Long-Term Background Referencing

Xiao

Zhu

et al. 2018

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Low-rank modeling is becoming popular and practical recently [35], due to its successful applications in many fields, such as data compression [17], subspace clustering [16], [36], image processing [37], [38], and multimedia analysis [39]. RPCA [17] is a representative low-rank modeling method.…”

Section: B Low-rank Modelingmentioning

confidence: 99%

Learning Robust and Discriminative Subspace With Low-Rank Constraints

2016

IEEE Trans. Neural Netw. Learning Syst.

127

View full text Add to dashboard Cite

Abstract-In this paper, we aim at learning robust and discriminative subspaces from noisy data. Subspace learning is widely used in extracting discriminative features for classification. However, when data are contaminated with severe noise, the performance of most existing subspace learning methods would be limited. Recent advances in low-rank modeling provide effective solutions for removing noise or outliers contained in sample sets, which motivates us to take advantage of low-rank constraints in order to exploit robust and discriminative subspace for classification. In particular, we present a discriminative subspace learning method called the supervised regularizationbased robust subspace (SRRS) approach, by incorporating the low-rank constraint. SRRS seeks low-rank representations from the noisy data, and learns a discriminative subspace from the recovered clean data jointly. A supervised regularization function is designed to make use of the class label information, and therefore to enhance the discriminability of subspace. Our approach is formulated as a constrained rank-minimization problem. We design an inexact augmented Lagrange multiplier optimization algorithm to solve it. Unlike the existing sparse representation and low-rank learning methods, our approach learns a low-dimensional subspace from recovered data, and explicitly incorporates the supervised information. Our approach and some baselines are evaluated on the COIL-100, ALOI, Extended YaleB, FERET, AR, and KinFace databases. The experimental results demonstrate the effectiveness of our approach, especially when the data contain considerable noise or variations.

show abstract

“…Hence, the optimization problem becomes to minimize: (4) Apparently, the grouping matrix discovers the weight description of the image that emphasizes the valuable areas for ranking. By featurewise weighting, the joint feature function in the ( with the subscript represents the auxiliary variable of samplewise neural network output corresponding to the input ) is given as: (5) where is the diagonal matrix with the elements of on the main diagonal. Since is the feature weights corresponding to the feature vector and , Eq.…”

Section: B Ranking-level: Ranking Connection With Feature Groupingmentioning

confidence: 99%

Multimedia Retrieval via Deep Learning to Rank

Zhao

Zhang

2015

IEEE Signal Process. Lett.

View full text Add to dashboard Cite

Many existing learning-to-rank approaches are incapable of effectively modeling the intrinsic interaction relationships between the feature-level and ranking-level components of a ranking model. To address this problem, we propose a novel joint learning-to-rank approach called Deep Latent Structural SVM (DL-SSVM), which jointly learns deep neural networks and latent structural SVM (connected by a set of latent feature grouping variables) to effectively model the interaction relationships at two levels (i.e., feature-level and ranking-level). To make the joint learning problem easier to optimize, we present an effective auxiliary variable-based alternating optimization approach with respect to deep neural network learning and structural latent SVM learning. Experimental results on several challenging datasets have demonstrated the effectiveness of the proposed learning to rank approach in real-world information retrieval.Index Terms-Deep neural network, joint learning, latent variable, learning to rank, structural SVM.

show abstract

Surveillance video coding via low-rank and sparse decomposition

Cited by 27 publications

References 6 publications

Towards Real-Time Service from Remote Sensing: Compression of Earth Observatory Video Data via Long-Term Background Referencing

Towards Real-Time Service from Remote Sensing: Compression of Earth Observatory Video Data via Long-Term Background Referencing

Learning Robust and Discriminative Subspace With Low-Rank Constraints

Multimedia Retrieval via Deep Learning to Rank

Contact Info

Product

Resources

About