Paper Info
Title
Classification of indecent videos by low complexity repetitive motion detection
Abstract
This paper proposes a fast method for detection of indecent video content using repetitive motion analysis. Unlike skin detection, motion will provide invariant features irrespective of race and color. The video material to be evaluated is divided into short fixed-length sections. By filtering different combinations of B-frame motion vectors using adjacency in time and space, one dominant motion vector is constructed for each frame. The power spectral density estimate of this dominant motion vector is then computed using a periodogram with a Hamming window. The resulting power spectrum is then subjected to a Slepian selection window to restrict the spectrum to a limited frequency range typical of indecent movement, as empirically derived by us. A threshold detector is then applied to detect repetitive motion in video sections. However, there are instances where repetitive motion occurs in these shorter sections without the video as a whole being indecent. As a second step, an additional detector can be employed to determine if the sections over a longer period of time can be classified as containing indecent material. The proposed method is resource efficient and do not require the typical IDCT step of video decoding. Further, the computationally expensive spectral estimation calculations are done using only one value per frame. Evaluations performed using a restricted set of videos show promising results with high true positive probability (≫85%) for a low false positive probability (≪10%) for the repetitive motion detection.
Year
DOI
Venue
2008
10.1109/AIPR.2008.4906438
AIPR
Keywords
Field
DocType
repetitive motion analysis,low complexity repetitive motion,video decoding,video section,indecent material,b-frame motion,repetitive motion,indecent video content,video material,repetitive motion detection,dominant motion vector,data mining,estimation,materials,power spectrum,spectral estimation,spectrum,hamming window,computer science,periodogram,image classification,false positive,power spectral density,image segmentation
Computer vision,Spectral density estimation,Quarter-pixel motion,Motion detection,Pattern recognition,Filter (signal processing),Artificial intelligence,Motion estimation,Motion analysis,Mathematics,Motion vector,Window function
Conference
ISSN
Citations 
PageRank 
1550-5219
11
0.67
References 
Authors
5
3
Name
Order
Citations
PageRank
Tadilo Endeshaw1110.67
Johan Garcia210112.66
Andreas Jakobsson3111.01