Video scene change detection using neural network: Improved ART2

Lee, Man Hee; Yoo, Hun Woo; Jang, Dong Sik

doi:10.1016/j.eswa.2005.09.031

Cited by 38 publications

(8 citation statements)

References 23 publications

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“…They commonly search for video shots by computing lowlevel features on entire or partitioned image frames, which are compared to those in consecutive frames to detect strong transitions (Lee, Yoo, & Jang, 2006;Zhu & Liu, 2009). Although low-level features are particularly useful for still image retrieval (Conci & Castro, 2002;Smeulders et al, 2000;Yoo et al, 2005) and video retrieval in movies, broadcast news, or sports (Xiong et al, 2006), they exhibit practical drawbacks for video surveillance.…”

Section: Related Workmentioning

confidence: 98%

Determining the best suited semantic events for cognitive surveillance

Fernández¹,

Baiget²,

Roca³

et al. 2011

Expert Systems with Applications

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Section: Related Workmentioning

confidence: 98%

Determining the best suited semantic events for cognitive surveillance

Fernández¹,

Baiget²,

Roca³

et al. 2011

Expert Systems with Applications

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“…Algorithms on compressed domain have the advantage of fast detection since the decompression time is unnecessary. However, these algorithms often show less performance since the features that can be extracted from the compressed video are limited [2] . The approaches in the article of [3] and [4] fall in this category.…”

Section: Introductionmentioning

confidence: 98%

A Method for Fast Shot Boundary Detection Based on SVM

Xue

Ouyang

et al. 2008

2008 Congress on Image and Signal Processing

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An algorithm for fast shot boundary detection based on SVM (Support Vector Machine) is proposed. In this algorithm, smooth intervals inside shots are first eliminated from the original video frame sequence. During this process, a gray variance sequence is extracted and serves as the basis for the detection of smooth intervals. Then a new frame sequence named RFS (Reordered Frame Sequence) is formed. Video features such as intensity pixel-wise difference, color histogram differences in HSV space and edge histogram differences in X and Y direction are extracted from RFS. These features are presented as input vectors to the SVM to implement the cut detection. After the cut detection, temporal multiresolution is applied to frames in RFS to fulfill the gradual change detection. Experimental results show that the proposed algorithm can implement the realtime shot boundary detection, while guarantee the performance.

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“…The matching will be achieved on sparse points in the stereo images. However, such a correct sparse matching can be applied in many stereovision applications such as construction of mosaic images, 9 rectification of stereo images, 10 change detection, 11 optical flow estimation etc. Most of these applications require the estimation of a transformation matrix called 'homography.'…”

Section: Introductionmentioning

confidence: 99%

Application of balanced neural tree for classifying tentative matches in stereo vision

et al. 2012

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Here, we present a new application of the supervised learning based classifier in stereo matching. In particular, the chosen classifier is a balanced neural tree. The tentative matches are obtained using the speeded-up robust feature (SURF) matching. The feature vector corresponding to each tentative match is formed based on a similarity measure between SURF descriptors and their neighborhoods in the two stereo images, and these feature vectors are classified into inlier or outlier classes. Further, accuracy of the obtained results have been evaluated in terms of stereovision applications such as in the estimation of the homography and rectification matrices between two stereo images. The experiments based on these stereo estimates show the applicability of the proposed application in the stereo vision

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Video scene change detection using neural network: Improved ART2

Cited by 38 publications

References 23 publications

Determining the best suited semantic events for cognitive surveillance

Determining the best suited semantic events for cognitive surveillance

A Method for Fast Shot Boundary Detection Based on SVM

Application of balanced neural tree for classifying tentative matches in stereo vision

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