Automatic detection and tracking of moving objects in complex environments for video surveillance applications

Autonomous video surveillance and monitoring has a rich history. A new method for detecting multiple moving objects based on improved background subtraction model and for tracking is based on feature based approach has proposed. Then identified moving objects are also counted, by indexing individually. The proposed algorithm is automatic and efficient in intelligent surveillance applications like vehicles monitoring, event recognition, and crime prevention, etc. The proposed model has proved to be robust in various environments (including indoor and outdoor scenes) and different types of background scenes. Experiments on real scenes show that the algorithm is effective for object detection and tracking.