The Guarantee Trusted Bank (GT Bank) at Opera Square, Accra, currently
faces challenges in accurately counting and monitoring customers
entering and exiting the bank premises, leading to operational
inefficiencies and safety concerns. Additionally, the current system for
counting and monitoring customers through both a CCTV and physical
approach in the bank is prone to errors and cannot collect and store
data for future planning. Security personnel also lack knowledge of the
exact number of daily visitors and struggle to determine the exact
timing of incidents during working and closing hours. This study aims to
explore the weaknesses of the current detection and counting system at
the GT bank and proposes solutions to address these issues. The primary
solution involves the implementation of a computerized human detection
and counting system, designed to accurately detect and count individuals
who enter and exit the GT bank’s branch premises. To achieve this goal,
a qualitative methodology is employed. Our qualitative method involved
interviewing participants using relevant open-ended questions, which
motivated them to express their thoughts and views openly with no
limitations. The analysis of quantitative data illustrates the
difficulties faced by the current system in detecting and counting
customers in the bank. Furthermore, the research participants are
willing to accept our proposed system, which would improve the current
counting and monitoring system used by the bank. Our proposed system
utilizes computer vision and deep learning algorithms through the use of
a single-shot multi-box detector (SSD) to identify human figures in
video feeds. By introducing the proposed system within the GT bank, it
can improve the overall customer experience. One implication for
decision-makers involves the designation of specialized personnel
responsible for system maintenance and troubleshooting, thereby
assisting in routine checks and upkeep to prevent system downtime and
maintain continuous operation.