Video-Based Gender Profiling on Challenging Camera Viewpoint for Restaurant Data Analytics

The customer gender composition is an important consideration for certain business decisions. This study highlights the feasibility of implementing a vision-based gender classification feature on top of a pre-existing people counting system that makes use of a CCTV camera in a retail establishment. The main problem lies in doing so using the fixed angled viewpoint of the restaurant's camera, which represents a practical use case as CCTV systems are often geared for security purposes instead of data analytics. Thus, these existing camera configurations are not ideal for existing computer vision models. A two-step neural networks-based approach, namely head detection via YOLOv2 and gender classification via Inception V3, is used to integrate this feature into the pre-existing system. The researchers partnered with a restaurant in a business district to obtain footage and test the system. The proponents were able to attain the target accuracy of 80%, albeit the feature's speed and accuracy can be further improved through the exploration of other object classification algorithms and through the use of downsizing and down sampling.