Abstract-Both detection and tracking objects are challenging problems because of the type of the objects and even their presence in the scene. Generally, object detection is a prerequisite for target tracking, and tracking has no effect on object detection. In this paper, we propose an algorithm to detect and track moving objects automatically of a video sequence analysis, taken with a fixed camera. In the detection steps we perform a background subtraction algorithm, the obtained results are decomposed using discrete stationary wavelet transform 2D and the coefficients are thresholded using Birge-Massart strategy. The tracking step is based on the classical Kalman filter algorithm. This later uses the Kalman filter as many as the number of the moving objects in the image frame. The tests evaluation proved the efficiency of our algorithm for motion detection using adaptive threshold. The comparison results show that the proposed algorithm gives a better performance of detection and tracking than the other methods.
Creating a system that can hear and respond accurately like a human is one of the most critical issues in human-computer interaction. This inspired the creation of the automatic speech recognition system, which uses efficient feature extraction and selection techniques to distinguish between different classes of speech signals. In order to improve the ASR (automatic speech recognition), the authors present a new feature extraction method in this study which is based on modified MFCC (mel frequency cepstral coefficients) using lifting wavelet transform LWT (lifting wavelet transform). The effectiveness of the proposed approach is verified using the datasets of the ATSSEE Research Unit “Analysis and Processing of Electrical and Energy Signals and Systems.” The experimental investigations have been carried out to demonstrate the practical viability of the proposed approach. Numerical and experimental studies concluded that the proposed approach is capable of detecting and localizing multiple under varying environmental conditions with noise-contaminated measurements.
Moving object detection is a fundamental task on smart CCTV systems, as it provides a focal point for further investigation. In this study, an algorithm for moving object detection in video, which is thresholded using a stationary wavelet transform (SWT), is developed. In the detection steps, the authors perform a background subtraction algorithm; the obtained results are decomposed using discrete stationary wavelet transform 2D, and the coefficients are thresholded using Birge-Massart strategy. This leads to an efficient calculation method and system compared to existing traffic estimation methods.
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