Video surveillance system against anti-terrorism by Using Adaptive Linear Activity Classification (ALAC) Technique

Automated human activity analysis has been, and remains, a challenging problem. Security and surveillance are essential issues in today's world. Any behavior which is uncommon in occurrence and deviates from customarily understood action could be termed as suspicious. For different application regions, while identifying human exercises, fundamentally three angles are taking in worry for human movement recognition system: Segmentation, feature extraction, and activity classification. This model aims at automatic detection of abnormal behavior in surveillance videos. In this proposed work adaptive linear activity classification method and internet of things (IoT) frameworks are used to detection human activities as well as to find out who is doing unusual activities. The enhanced plan of the built environment condition will give a better observation. Such framework can be actualized in peoples in general places, for example, shopping centers, airports, and railway station or any private premises where security is the prime concern. The proposed ALAC method validated through simulation using MATLAB and VB.net software. Its ability to detect the activity of human the simulation result shows the effectiveness using ALAC method, Overall 97% efficiency achieved by using ALAC method.