Detection of a moving human is challenging for real-time systems. Misdetection in high alert security areas may lead to heavy losses. This paper presents an optimized approach to avoid this misdetection in sensitive areas. Rotation invariant optimized correlation filters are used for detection of humans. Some pre-processing algorithms such as background subtraction and color space conversion have been linked to the correlation filters to minimize processing time and maximize the accuracy of target detection. The experimental tests of the proposed methodology validate that better accuracy can be achieved if the proposed optimized approach is utilized for moving human detection in real-time systems. In future work, the proposed approach will be extended to detect human activity at night and thermal imagery.
CPU performance is estimated from the execution of processes per unit time. The selection of the CPU scheduling algorithm in less time is a vital issue. In this paper, a novel approach has been proposed in which selection of an appropriate CPU scheduling algorithm is done through machine learning algorithms dynamically. The result of the proposed algorithm is shown in the experimental section. Through experimentation, it is found that a decision tree gives better results in terms of accuracy and computational time as compared to other machine learning algorithms.
Correlation filters due to its three protuberant advantages have proven very effective for automatic target detection, biometric verification and security applications. In this paper, correlation filters are implemented in hardware FPGA keeping in view their importance in real time applications. Hardware implementation results are placed in comparison with results generated through software. These results are almost similar with a negligible variation i.e. 10-4, which is demonstrated in the experimental section, in addition to valuable time reduction. The hardware design of these filters is implemented in LabView which can be subsequently employed in real-time security applications. This design may be expanded for other advanced variants of correlation filters in future work.
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