  Service industries contribute significantly to many developed and developing
- economies. As their business activities expand rapidly, many service
companies struggle to maintain customer's satisfaction due to sluggish service
response caused by resource shortages. Anticipating resource shortages and
proffering solutions before they happen is an effective way of reducing the
adverse effect on operations. However, this proactive approach is very
expensive in terms of capacity and labor costs. Many companies fall into
productivity conundrum as they fail to find sufficient strong arguments to
justify the cost of a new technology yet cannot afford not to invest in new
technologies to match up with competitors. The question is whether there is an
innovative solution to maximally utilize available resources and drastically
reduce the effect that the shortages of resources may cause yet achieving high
level of service quality at a low cost. This work demonstrates with a practical
analysis of a trolley tracking system we designed and deployed at Hong Kong
International Airport (HKIA) on how video analytics helps achieve management's
goal of satisfying customer's needs via real-time detection and prevention of
problems they may encounter during the service consumption process using
existing video technology rather than adopting new technologies. This paper
presents the integration of commercial video surveillance system with deep
learning algorithms for video analytics. We show that our system can provide
accurate decision when faced with total or partial occlusion with high accuracy
and it significantly improves daily operation. It is envisioned that this work
will heighten the appreciation of integrative technologies for resource
management within the service industries and as a measure for real-time
customer assistance.
