Affiliations: [a] Department of Computer Science, Amity University, Gurgaon, India
| [b] The NorthCap University, Gurgaon, India
| [c] Department of Computer Science, ASET, Amity University, Noida, India
| [d] Department of ECE, GL Bajaj Institute of Technology and Management, Greater Noida, India
Correspondence:
[*]
Corresponding author. Dr. Akansha Singh, Assoc. Prof., Department of Computer Science, ASET, Amity University, Noida, India. E-mail: [email protected].
Abstract: This paper presents an automated vehicle identification and classification method from traffic videos. The proposed method unlike other traditional methods combines the multiple time spatial frames to detect moving objects. These moving objects are the potential vehicles however there may be some other moving objects also. Therefore to further improve the accuracy of the proposed method, the moving objects are classified using object oriented classification scheme. The identification of vehicles from traffic videos plays an important role in Intelligent Transport systems (ITS). A virtual line is placed on each frame such that the objects crossing this line are the desired moving objects. The object based classifier makes use of fuzzy rules based on features like area, perimeter, and elongation and so on. These fuzzy rules are used to classify them into vehicle and non-vehicle classes. The second level of classification further classifies the vehicles into two wheeler, four wheeler and six wheeler vehicles. The method can be appropriately used for traffic surveillance as it also computes the speed of vehicles using the time spatial frames. The proposed method is applied on traffic videos of multiple time lengths. A comparative study of the proposed method with the existing methods reveals that the proposed work has higher accuracy. The motion detection, vehicle classification and speed of computation make this method best suited for many ITS applications like traffic surveillance and other similar applications.
Keywords: Vehicle detection, multiple spatial time frames, object oriented classification, rule based method, motion detection
