Shot Detection Using Pixel wise Difference with Adaptive Threshold and Color Histogram Method in Compressed and Uncompressed Video

Patel, Upesh; Shah, Pratik; Panchal, P.

doi:10.5120/10625-5347

Cited by 12 publications

(6 citation statements)

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“…Shot boundary detection based on colour histograms computation [15] between frames is used in the proposed work as a measure of discontinuity. Since pixel based frame comparison is very sensitive to moving object or camera motion.…”

Section: Candidate Frame Detectionmentioning

confidence: 99%

Robust candidate frame detection in videos using semantic content modeling

Manonmani¹,

Mala²

2014

2014 International Conference on Communication and Network Technologies

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Videos are of the most popular rich media formats carrying large amount of visual, audio and textual information. In recent years people all over the world show great interest in video mining to extract meaningful patterns and knowledge to enhance the smart level of video applications. In this work Speeded Up Robust Features (SURF) are used to detect the candidate frames among the set of key frames extracted from a video content. By eliminating the presence of duplicate key frames the computational and time complexity of processing a large number of frames is reduced. From the identified candidate frames semantic objects with meaningful content are extracted which improves the efficiency of video mining applications like Video recommendation systems, Video concept detection etc. Experimental results show that the proposed approach eliminates the duplicate frames without a prior knowledge of the video content.

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Section: Candidate Frame Detectionmentioning

confidence: 99%

Robust candidate frame detection in videos using semantic content modeling

Manonmani¹,

Mala²

2014

2014 International Conference on Communication and Network Technologies

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“…Secondly, what type of query given by user should be resolved? The types of query are text based, image based, object based, sketch based, shot based and scene based queries [21]. The objective of CBVR system is to retrieve the data base videos whose content is similar to that of user query.…”

Section: Cbvr System Designmentioning

confidence: 99%

A Scheme for Shot Detection and Video Retreival using Spatio Temporal Features

2019

IJRTE

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There has been a revolution in multimedia with technological advancement. Hence, Video recording has increased in leaps and bounds. Video retrieval from a huge database is cumbersome by the existing text based search since a lot of human effort is involved and the retrieval efficiency is meager as well. In view of the present challenges, video retrieval based on video content prevails over the existing conventional methods. Content implies real video information such as video features. The performance of the Content Based Video Retrieval (CBVR) depends on Feature extraction and similar features matching. Since the selection of features in the existing algorithms is not effective, the retrieval processing time is more and the efficiency is less. Combined features of color and motion have been proposed for feature extraction and Spatio-Temporal Scale Invariant Feature Transform is used for Shot Boundary Detection. Since the characteristic of color feature is visual video content and that of motion feature is temporal content, these two features are significant in effective video retrieval. The performance of the CBVR system has been evaluated on the TRECVID dataset and the retrieved videos reveal the effectiveness of proposed algorithm.

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“…The gray tones are integer values included in the

range. After that, it computes the histogram of such differences (line 4) using function histogram [ 41 ]. Finally, if this histogram stores any value larger than 0 in the first 5 bins (function light , line 5), the procedure sets the transition field of that cell (cell.transition) equal to the integer value time (line 6).…”

Section: Static (Without Memory) Movement Detectormentioning

confidence: 99%

Moving Object Detection in Heterogeneous Conditions in Embedded Systems

Garbo

Quer

2017

Sensors

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This paper presents a system for moving object exposure, focusing on pedestrian detection, in external, unfriendly, and heterogeneous environments. The system manipulates and accurately merges information coming from subsequent video frames, making small computational efforts in each single frame. Its main characterizing feature is to combine several well-known movement detection and tracking techniques, and to orchestrate them in a smart way to obtain good results in diversified scenarios. It uses dynamically adjusted thresholds to characterize different regions of interest, and it also adopts techniques to efficiently track movements, and detect and correct false positives. Accuracy and reliability mainly depend on the overall receipt, i.e., on how the software system is designed and implemented, on how the different algorithmic phases communicate information and collaborate with each other, and on how concurrency is organized. The application is specifically designed to work with inexpensive hardware devices, such as off-the-shelf video cameras and small embedded computational units, eventually forming an intelligent urban grid. As a matter of fact, the major contribution of the paper is the presentation of a tool for real-time applications in embedded devices with finite computational (time and memory) resources. We run experimental results on several video sequences (both home-made and publicly available), showing the robustness and accuracy of the overall detection strategy. Comparisons with state-of-the-art strategies show that our application has similar tracking accuracy but much higher frame-per-second rates.

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Shot Detection Using Pixel wise Difference with Adaptive Threshold and Color Histogram Method in Compressed and Uncompressed Video

Cited by 12 publications

References 1 publication

Robust candidate frame detection in videos using semantic content modeling

Robust candidate frame detection in videos using semantic content modeling

A Scheme for Shot Detection and Video Retreival using Spatio Temporal Features

Moving Object Detection in Heterogeneous Conditions in Embedded Systems

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