A novel efficient algorithm for duplicate video comparison in surveillance video storage systems

This paper investigates and analyzes the characteristics of video data and puts forward a campus surveillance video storage system with the university campus as the specific application environment. Aiming at the challenge that the content-based video retrieval response time is too long, the key-frame index subsystem is designed. The key frame of the video can reflect the main content of the video. Extracted from the video, key frames are associated with the metadata information to establish the storage index. The key-frame index is used in lookup operations while querying. This method can greatly reduce the amount of video data reading and effectively improves the query’s efficiency. From the above, we model the storage system by a stochastic Petri net (SPN) and verify the promotion of query performance by quantitative analysis.

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A Computationally Efficient Algorithm for Large Scale Near-Duplicate Video Detection

MultiMedia Modeling - Lecture Notes in Computer Science ◽

10.1007/978-3-319-14442-9_53 ◽

2015 ◽

pp. 481-490 ◽

Cited By ~ 2

Author(s):

Dawei Liu ◽

Zhihua Yu

Keyword(s):

Efficient Algorithm ◽

Large Scale ◽

Computationally Efficient ◽

Video Detection ◽

Duplicate Video

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Creating a standard method of controlling digital microscopes: the microscope access protocol (map)

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100136453 ◽

1995 ◽

Vol 53 ◽

pp. 16-17

Author(s):

T. A. Dodson ◽

E. Völkl ◽

L. F. Allard ◽

T. A. Nolan

Keyword(s):

Data Storage ◽

Storage Systems ◽

Digital Systems ◽

Data Networks ◽

Digital Microscopy ◽

Command Language ◽

Access Protocol ◽

Analog To Digital ◽

Basic Physics ◽

Scanning Electron

The process of moving to a fully digital microscopy laboratory requires changes in instrumentation, computing hardware, computing software, data storage systems, and data networks, as well as in the operating procedures of each facility. Moving from analog to digital systems in the microscopy laboratory is similar to the instrumentation projects being undertaken in many scientific labs. A central problem of any of these projects is to create the best combination of hardware and software to effectively control the parameters of data collection and then to actually acquire data from the instrument. This problem is particularly acute for the microscopist who wishes to "digitize" the operation of a transmission or scanning electron microscope. Although the basic physics of each type of instrument and the type of data (images & spectra) generated by each are very similar, each manufacturer approaches automation differently. The communications interfaces vary as well as the command language used to control the instrument.

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Computational Micrograph Registration with Sieve Processes

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100164660 ◽

1996 ◽

Vol 54 ◽

pp. 440-441

Author(s):

P.J. Phillips ◽

J. Huang ◽

S. M. Dunn

Keyword(s):

Planar Graph ◽

Efficient Algorithm ◽

Spatial Organization ◽

Spatial Arrangement ◽

Stereo Image ◽

Image Model ◽

Geometric Invariants ◽

Point Set

In this paper we present an efficient algorithm for automatically finding the correspondence between pairs of stereo micrographs, the key step in forming a stereo image. The computation burden in this problem is solving for the optimal mapping and transformation between the two micrographs. In this paper, we present a sieve algorithm for efficiently estimating the transformation and correspondence.In a sieve algorithm, a sequence of stages gradually reduce the number of transformations and correspondences that need to be examined, i.e., the analogy of sieving through the set of mappings with gradually finer meshes until the answer is found. The set of sieves is derived from an image model, here a planar graph that encodes the spatial organization of the features. In the sieve algorithm, the graph represents the spatial arrangement of objects in the image. The algorithm for finding the correspondence restricts its attention to the graph, with the correspondence being found by a combination of graph matchings, point set matching and geometric invariants.

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