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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ozlq63ehnjeqxf6cuxxn27cqra" style="color: black;">Information Sciences</a>
CCTV (Closed-Circuit TeleVision) systems are broadly deployed in the present world. To ensure in-time reaction for intelligent surveillance, it is a fundamental task for real-world applications to determine the gender of people of interest. However, normal video algorithms for gender profiling (usually face profiling) have three drawbacks. First, the profiling result is always uncertain. Second, the profiling result is not stable. The degree of certainty usually varies over time, sometimes even<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.ins.2015.11.011">doi:10.1016/j.ins.2015.11.011</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/kyvt3bd3xzfobfdxxegcjhr4fy">fatcat:kyvt3bd3xzfobfdxxegcjhr4fy</a> </span>
more »... to the extent that a male is classified as a female, and vice versa. Third, for a robust profiling result in cases that a person's face is not visible, other features, such as body shape, are required. These algorithms may provide different recognition results -at the very least, they will provide different degrees of certainties. To overcome these problems, in this paper, we introduce an Dempster-Shafer (DS) evidential approach that makes use of profiling results from multiple algorithms over a period of time, in particular, Denoeux's cautious rule is applied for fusing mass functions through time lines. Experiments show that this approach does provide better results than single profiling results and classic fusion results. Furthermore, it is found that if severe mis-classification has occurred at the beginning of the time line, the combination can yield undesirable results. To remedy this weakness, we further propose three extensions to the evidential approach proposed above incorporating notions of time-window, time-attenuation, and time-discounting, respectively. These extensions also applies Denoeux's rule along with time lines and take the DS approach as a special case. Experiments show that these three extensions do provide better results than their predecessor when mis-classifications occur.
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