Identify suspicious behavior of individuals at ATMs, using real-time CCTV footage. Model needed to run on a Raspberry Pi.
- 1.Developed a DL model that can identify the location of people in a video.
- 2.Developed a DL model to identify facial key points of a people present within the ATM.
- 3.Developed light weight models, so that the solution can be implemented on edge device like Raspberry Pi.
- 1.Designed a deep learning model to analyze video footage and accurately determine the location of a person within each frame.
- 2.Designed a deep learning model to analyze people’s face in order to identify key facial points on the face.
- 3.Designed a pipeline to use the features identified by both deep learning models to determine fraudulence.
- 4.Ensured that the models were lightweight so that the models could run on a edge device such as Raspberry Pi.
98% accuracy in identifying people and 97% accuracy in identifying facial key points.