Extracting Key Frames for Surveillance Video Based on Color Spatial Distribution Histograms

2009 ◽  
pp. 1005-1010 ◽  
Author(s):  
Jun Chang ◽  
RuiMin Hu ◽  
ZhongYuan Wang ◽  
Bo Hang
Keyword(s):  
Spatial Distribution ◽  
Surveillance Video ◽  
Key Frames

A Novel Framework for Efficient Extraction of Meaningful Key Frames From Surveillance Video

2019 ◽  
pp. 342-359
Author(s):  
Suresh Chandra Raikwar ◽  
Charul Bhatnagar ◽  
Anand Singh Jalal
Keyword(s):  
Computational Cost ◽  
Extraction Methods ◽  
Surveillance Video ◽  
Distribution Method ◽  
Color Distribution ◽  
Key Frame Extraction ◽  
Key Frame ◽  
Efficient Extraction ◽  
Key Frames ◽  
Edge Histogram

The key frame extraction, aimed at reducing the amount of information from a surveillance video for analysis by human. The key frame is an important frame of a video to provide an overview of the video. Extraction of key frames from surveillance video is of great interest in effective monitoring and later analysis of video. The computational cost of the existing methods of key frame extraction is very high. The proposed method is a framework for Key frame extraction from a long surveillance video with significantly reduced computational cost. The proposed framework incorporates human intelligence in the process of key frame extraction. The results of proposed framework are compared with the results of IMARS (IBM multimedia analysis and retrieval system), results of the key frame extraction methods based on entropy difference method, spatial color distribution method and edge histogram descriptor method. The proposed framework has been objectively evaluated by fidelity. The experimental results demonstrate evidence of the effectiveness of the proposed approach.

A Feature Extraction Approach of Traffic Congestion from Video

2012 ◽  
Vol 490-495 ◽  
pp. 1058-1062
Author(s):  
Yuan Feng Yang ◽  
Xue Feng Xian ◽  
Li Li Liao ◽  
Min Ya Zhao
Keyword(s):  
Traffic Congestion ◽  
Clustering Algorithm ◽  
High Accuracy ◽  
Surveillance Video ◽  
Traffic Surveillance ◽  
Flow Energy ◽  
Congestion Detection ◽  
Novel Approach ◽  
Traffic Video ◽  
Key Frames

To avoid the difficulty of collecting accurate traffic flow data, this paper proposes a novel approach for congestion features extraction from traffic video. The approach firstly segments the traffic video into shots and the shot motion content feature is extracted. Then, we extract the key frames applying an improved global k-means clustering algorithm. The last congestion feature of the global optical flow energy is computed based on the key frames. The numerical experiments on traffic surveillance video show the validity and high accuracy for traffic congestion detection using the propose method in this paper

A Novel Framework for Efficient Extraction of Meaningful Key Frames from Surveillance Video

2015 ◽  
Vol 4 (2) ◽  
pp. 56-73 ◽  
Author(s):  
Suresh Chandra Raikwar ◽  
Charul Bhatnagar ◽  
Anand Singh Jalal
Keyword(s):  
Computational Cost ◽  
Extraction Methods ◽  
Surveillance Video ◽  
Distribution Method ◽  
Color Distribution ◽  
Key Frame Extraction ◽  
Key Frame ◽  
Efficient Extraction ◽  
Key Frames ◽  
Edge Histogram

The key frame extraction, aimed at reducing the amount of information from a surveillance video for analysis by human. The key frame is an important frame of a video to provide an overview of the video. Extraction of key frames from surveillance video is of great interest in effective monitoring and later analysis of video. The computational cost of the existing methods of key frame extraction is very high. The proposed method is a framework for Key frame extraction from a long surveillance video with significantly reduced computational cost. The proposed framework incorporates human intelligence in the process of key frame extraction. The results of proposed framework are compared with the results of IMARS (IBM multimedia analysis and retrieval system), results of the key frame extraction methods based on entropy difference method, spatial color distribution method and edge histogram descriptor method. The proposed framework has been objectively evaluated by fidelity. The experimental results demonstrate evidence of the effectiveness of the proposed approach.

Alphabet Computer Automatic Clarity Algorithm

2014 ◽  
Vol 556-562 ◽  
pp. 3905-3908
Author(s):  
Min Cao
Keyword(s):  
Frequency Domain ◽  
Surveillance Video ◽  
Key Frames ◽  
Computer Recognition

The alphabet computer recognition methods are widely applied in many areas. The clarity of the alphabet is first key step. This paper proposes a kind of image clarity algorithm for alphabet. The key frames in the surveillance video are analyzed in frequency domain to identify the key parameters causing alphabet illegibility and restore the surveillance video. The experiment results illustrate the algorithm can well restore the key frames of the alphabet in the video which can be widely applied in the vehicle plate recognition.

scholarly journals Searching surveillance video contents using convolutional neural network

2021 ◽  
Vol 11 (2) ◽  
pp. 1656
Author(s):  
Duaa Mohammad ◽  
Inad Aljarrah ◽  
Moath Jarrah
Keyword(s):  
Intelligent System ◽  
Surveillance Systems ◽  
Surveillance Video ◽  
Text File ◽  
Sobel Operator ◽  
Order Of Magnitude ◽  
Key Frames ◽  
Video Searching ◽  
Search For Objects

Manual video inspection, searching, and analyzing is exhausting and inefficient. This paper presents an intelligent system to search surveillance video contents using deep learning. The proposed system reduced the amount of work that is needed to perform video searching and improved the speed and accuracy. A pre-trained VGG-16 CNNs model is used for dataset training. In addition, key frames of videos were extracted in order to save space, reduce the amount of work, and reduce the execution time. The extracted key frames were processed using the sobel operator edge detector and the max-pooling in order to eliminate redundancy. This increases compaction and avoids similarities between extracted frames. A text file, that contains key frame index, time of occurrence, and the classification of the VGG-16 model, is produced. The text file enables humans to easily search for objects of interest. VIRAT and IVY LAB datasets were used in the experiments. In addition, 128 different classes were identified in the datasets. The classes represent important objects for surveillance systems. However, users can identify other classes and utilize the proposed methodology. Experiments and evaluation showed that the proposed system outperformed existing methods in an order of magnitude. The system achieved the best results in speed while providing a high accuracy in classification.

Lithography below 100 nm

1983 ◽  
Vol 41 ◽  
pp. 96-99
Author(s):  
L. D. Jackel
Keyword(s):  
Spatial Distribution ◽  
Electron Beam ◽  
Electron Scattering ◽  
Electron Beam Lithography ◽  
Substrate Material ◽  
Local Electron ◽  
Electron Dose ◽  
Positive Resist ◽  
Exposure Process

Most production electron beam lithography systems can pattern minimum features a few tenths of a micron across. Linewidth in these systems is usually limited by the quality of the exposing beam and by electron scattering in the resist and substrate. By using a smaller spot along with exposure techniques that minimize scattering and its effects, laboratory e-beam lithography systems can now make features hundredths of a micron wide on standard substrate material. This talk will outline sane of these high- resolution e-beam lithography techniques.We first consider parameters of the exposure process that limit resolution in organic resists. For concreteness suppose that we have a “positive” resist in which exposing electrons break bonds in the resist molecules thus increasing the exposed resist's solubility in a developer. Ihe attainable resolution is obviously limited by the overall width of the exposing beam, but the spatial distribution of the beam intensity, the beam “profile” , also contributes to the resolution. Depending on the local electron dose, more or less resist bonds are broken resulting in slower or faster dissolution in the developer.

SEM evaluation of the TEM cold-stage double-film specimen

1984 ◽  
Vol 42 ◽  
pp. 272-273
Author(s):  
Jayesh Bellare
Keyword(s):  
Spatial Distribution ◽  
Sample Preparation ◽  
Sample Thickness ◽  
Specimen Preparation ◽  
Preparation Technique ◽  
Liquid Sample ◽  
Thin Specimen ◽  
Sample Preparation Technique ◽  
Cold Stage ◽  
Film Specimen

Seeing is believing, but only after the sample preparation technique has received a systematic study and a full record is made of the treatment the sample gets.For microstructured liquids and suspensions, fast-freeze thermal fixation and cold-stage microscopy is perhaps the least artifact-laden technique. In the double-film specimen preparation technique, a layer of liquid sample is trapped between 100- and 400-mesh polymer (polyimide, PI) coated grids. Blotting against filter paper drains excess liquid and provides a thin specimen, which is fast-frozen by plunging into liquid nitrogen. This frozen sandwich (Fig. 1) is mounted in a cooling holder and viewed in TEM.Though extremely promising for visualization of liquid microstructures, this double-film technique suffers from a) ireproducibility and nonuniformity of sample thickness, b) low yield of imageable grid squares and c) nonuniform spatial distribution of particulates, which results in fewer being imaged.

Macroprobe Mode for the Study of Segregation in Steels

1990 ◽  
Vol 48 (2) ◽  
pp. 260-261
Author(s):  
Auclair Gilles ◽  
Benoit Danièle
Keyword(s):  
Mechanical Properties ◽  
Spatial Distribution ◽  
High Performance ◽  
Volume Fraction ◽  
Sheet Steel ◽  
Acquisition Time ◽  
Chemical Information ◽  
X Ray ◽  
Accurate Quantitative Analysis ◽  
Optical Micrographs

During these last 10 years, high performance correction procedures have been developed for classical EPMA, and it is nowadays possible to obtain accurate quantitative analysis even for soft X-ray radiations. It is also possible to perform EPMA by adapting this accurate quantitative procedures to unusual applications such as the measurement of the segregation on wide areas in as-cast and sheet steel products.The main objection for analysis of segregation in steel by means of a line-scan mode is that it requires a very heavy sampling plan to make sure that the most significant points are analyzed. Moreover only local chemical information is obtained whereas mechanical properties are also dependant on the volume fraction and the spatial distribution of highly segregated zones. For these reasons we have chosen to systematically acquire X-ray calibrated mappings which give pictures similar to optical micrographs. Although mapping requires lengthy acquisition time there is a corresponding increase in the information given by image anlysis.

Ultrastructural analysis of the spatial distribution of MRNA

1992 ◽  
Vol 50 (1) ◽  
pp. 552-553
Author(s):  
Gary Bassell ◽  
Robert H. Singer
Keyword(s):  
Electron Microscopy ◽  
Spatial Distribution ◽  
In Situ Hybridization ◽  
High Resolution ◽  
Nucleic Acids ◽  
Colloidal Gold ◽  
Dna Probe ◽  
Nucleotide Analogs ◽  
Gold Particles

We have been investigating the spatial distribution of nucleic acids intracellularly using in situ hybridization. The use of non-isotopic nucleotide analogs incorporated into the DNA probe allows the detection of the probe at its site of hybridization within the cell. This approach therefore is compatible with the high resolution available by electron microscopy. Biotinated or digoxigenated probe can be detected by antibodies conjugated to colloidal gold. Because mRNA serves as a template for the probe fragments, the colloidal gold particles are detected as arrays which allow it to be unequivocally distinguished from background.

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