No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Moving object detection and description
... Then, the spatial and temporal information are fused using STP algorithm. STP is estimated using the average technique [22]; it can be computed by applying two steps: spatial 190 averaging followed by temporal averaging. The spatial averaging depends on the group operation (where it calculates new pixel values from neighborhoods by using a template convolution) which applies to each frame in the shot as follows: ...
Surveillance cameras are widely used to provide protection and security; also their videos are used as strong evidence in the courts. Through the availability of video editing tools, it has become easy to distort these evidence. Sometimes, to hide the traces of forgery, some post-processing operations are performed after editing. Hence, the authenticity and integrity of surveillance videos have become urgent to scientifically validate. In this paper, we propose inter-frame forgeries (frame deletion, frame insertion, and frame duplication) detection system using 2D convolution neural network (2D-CNN) of spatiotemporal information and fusion for deep automatically feature extraction; Gaussian RBF multi-class support vector machine (RBF-MSVM) is used for the classification process. The experimental results show that the efficiency of the proposed system for detecting all inter-frame forgeries, even when the forged videos have undergone additional post-processing operations such as Gaussian noise, Gaussian blurring, brightness modifications, and compression.
... Background subtraction and frame difference were used is this research. The basic way to separate a moving object from its background is to subtract the background from the image, leaving just the moving object or the foreground [11]. Background subtraction pixels from background subtraction can be extracted; thus, the moving object or human detection in this research can be defined by: ...
Image processing is mostly used for exploring image behaviour. There are several steps in image processing. Image acquisition, pre-processing, feature extraction, and classification are the processes used for the detection of human movement based on high-level feature extraction (HLFE), in which HLFE was used for feature extraction in this paper. This study proposed the use of background subtraction and frame difference. This research was conducted to analyse the difference of background subtraction and frame difference methods based on movement of human. Movement of human detected by using feature extraction were centroid image technique used. Furthermore, support vector machine (SVM) was used for classification.
ResearchGate has not been able to resolve any references for this publication.