Arman Hosseinzadeh, - Computer Engineering and Information Technology Department Amirkabir University of Technology Tehran, Iran
Reza Safabakhsh - Computer Engineering and Information Technology Department Amirkabir University of Technology Tehran, Iran

Traffic video analysis has turned into one of the most challenging fields in machine vision and intelligent transportation systems. Vehicle counting and classification, motion analysis andvehicle interaction understanding are some of the objectives that caused installation of cameras on intersections. As a strong basisfor semantic analysis of videos, we need a model that can describe the scene in terms of zones and paths where moving objects mustfit in. To gain this model a new robust approach for denoising input video is proposed that shows impressive improvement inresults of zone learning and raised the success rate of correctzone detection to 93%. The motion path patterns are learned from the filtered vehicle trajectories based on learned model. The success rate of this stage is also raised to 93% because of great performance of zone learning.

Vehiclie Trajectory; Entry ، Exit Zones; Traffic Monitoring; Path Learning