Indian Journal of Science and Technology
Year: 2016, Volume: 9, Issue: 47, Pages: 1-16
Surbhi Kapoor1 , P. K. Pateria1 and G. Singh2
1 Department of Computer Science and Engineering, Lovely Professional University, Phagwara - 144411, Punjab, India; [email protected], [email protected] 2 School of Electronics and Electical Engineering, Lovely Professional University, Phagwara - 144411, Punjab, India; [email protected]
Objectives: The system aims at developing an intelligent video surveillance system which analyses the behavior of moving object and classifies it as standing, walking (normal behavior) and bending (abnormal behavior). The proposed system is also capable of detecting the persons moving into the restricted region providing the user with an ease to select the area of its own choice, thereby eliminating the problem of static region of interest. Methods/Statistical Analysis: In this paper we have detected the moving objects and tracked them using Kalman Filter on the basis of id assigned to each moving object. The direction of the moving object is labeled as right, left, up and down. The experiments are conducted in the corridors and pathways at Lovely Professional University on various persons with different heights. Neural network is used to validate our work and helps us in setting an experimental threshold which decides when the person bends or walks. This eliminates the need of any special detector which estimates the human pose, thereby reducing the cost. The neural network produces an efficiency above 95% which is quite promising. The implemented system follows a new approach of eradicating the problem of static region of interest and helps us to generate an alert when a person enters into an illegal entry zone. Findings: The objective of the proposed work is to turn the passive cameras into active cameras. The system finds out the direction of the moving object with an accuracy lying between 86.9% and 100%. The existing systems deploy special detectors for pose estimation making the system very costly. The implemented system eradicates the need of such detectors. Neural network validates the abnormal bending behavior producing an efficiency above 95%. The proposed approach eliminates the problem of static region by allowing the user to define its own region of interest before the process starts. This region of interest is regarded as restricted region. When the person enters into the restricted region, the system generates an alert and marks it as illegal entry. This will reduce the time of the humans to monitor every single camera installed in the campus as the system will generate an alert in case of an illegal entry which is regarded as an abnormal event. There are three events in the dataset for illegal entry and the system detects all the three events correctly. We can also track the trajectory of every individual which can be useful if any mischievous activity is performed by that individual. Application/Improvements: With intelligent and automated systems, performance can be increased and cost can be reduced. The manpower can be reduced, thus increasing accuracy. The implemented system has its shortcomings as if it leaves the frame and enters again, the system will assign it a new id. The accuracy of calculating the direction can be improved.
Keywords: Background Subtraction, Kalman Filter, Region of Interest, Surveillance, Trajectory
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