Hybrid Approach for Shelf Monitoring and Planogram Compliance (Hyb-SMPC) in Retails Using Deep Learning and Computer Vision

<table class="table-group" id="tab7"><tr><td><table class="table"><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr class="thead"><td class="align_left">Product size</td><td class="align_center">Recurring patterns (%)</td><td class="align_center">Hyb-SMPC (%)</td></tr><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr><td class="align_left">Small</td><td class="align_center">95.32</td><td class="align_center">99</td></tr><tr><td class="align_left">Medium</td><td class="align_center">90.61</td><td class="align_center">97</td></tr><tr><td class="align_left">Large</td><td class="align_center">85.24</td><td class="align_center">98</td></tr><tr class="table-tr"><td colspan="3"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Comparison of <i>compliance accuracy</i> of Hyb-SMPC with conventional method.</div>

Mathematical Problems in Engineering

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Table 7

Table 7: Hybrid Approach for Shelf Monitoring and Planogram Compliance (Hyb-SMPC) in Retails Using Deep Learning and Computer Vision 