Recognition of Suspicious Human Activities using KLT and Kalman Filter for ATM Surveillance System

one of the active research areas in the field of Computer Vision in today's era is recognizing human activity under video surveillance. To resolve suspicious activity, sensitive and public places such as school, college, jewellery store, railway stations, a temple, bank, etc. can be monitored using video surveillance. It is mind-numbing and time-consuming to track such public areas for a long time. One such area is the Automated Teller Machine (ATM), monitored by a surveillance system. An intelligent monitoring system is proposed to classify real-time based human behaviour and categories them into regular and unusual activities to ensure the safety aspect of ATM and can cause different levels of alarm. This paper proposes a real-time system using the Kalman Filter and the Kanade-Lucas-Tomasi (KLT) Tracking Algorithm to detect and monitor suspicious or non-suspicious human behaviour for ATM video surveillance. On a real-time ATM Surveillance database, experimental results are carried out.