Target Tracking In Real Time Surveillance Cameras and Videos
원문 초록
Security concerns has been kept on increasing, so it is important for everyone to keep their property safe from thefts and destruction. So the need for surveillance techniques are also increasing. The system has been developed to detect the motion in a video. A system has been developed for real time applications by using the techniques of background subtraction and frame differencing. In this system, motion is detected from the webcam or from the real time video. Background subtraction and frames differencing method has been used to detect the moving target. In background subtraction method, current frame is subtracted from the referenced frame and then the threshold is applied. If the difference is greater than the threshold then it is considered as the pixel from the moving object, otherwise it is considered as background pixel. Similarly, two frames difference method takes difference between two continuous frames. Then that resultant difference frame is thresholded and the amount of difference pixels is calculated.
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Autoencoder-based background reconstruction and foreground segmentation with background noise estimation
Robust Graph Learning from Noisy Data
Deeply Learned Robust Matrix Completion for Large-scale Low-rank Data Recovery
Learning Spatial-Temporal Regularized Tensor Sparse RPCA for Background Subtraction
Efficient Low-Rank Matrix Factorization based on l1,ε-norm for Online Background Subtraction
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