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Smart city article details
| Title | Vehicle Identification Using Modified Region Based Convolution Network For Intelligent Transportation System |
|---|---|
| ID_Doc | 60924 |
| Authors | Sharma P.; Singh A.; Singh K.K.; Dhull A. |
| Year | 2022 |
| Published | Multimedia Tools and Applications, 81, 24 |
| DOI | http://dx.doi.org/10.1007/s11042-020-10366-x |
| Abstract | Intelligent transportation systems (ITS) are the integration of information and communications technologies with applications which are significant in traffic control and management. The increased number of on road vehicles in urban areas urges the need of development of automated methods for traffic management. Vehicle identification, classification and analysis enable the intelligent transportation systems to make decisions. In this paper, an automated method for video analysis for vehicle identification using a modified Region based Convolution Neural Network (RCNN) has been proposed. The traffic videos collected by CCTV cameras installed on the roads are analyzed for vehicle identification in a given frame. The pretrained google net is used to extract features. These features are used by the Region based Convolution Neural Network for vehicle identification. The vehicles are identified using probability score computed using intersection of objects (IoU). The identified vehicles are classified into ten different vehicle classes. The proposed network concatenates features from previous layers to reduce loss and consequently improve the vehicle identification accuracy. The vehicle identification method is further extended for vehicle counting and behavioral analysis. The vehicle counting information can be used for congestion control in smart cities. The behavioral analysis includes computation of speed of vehicles. The speed information is useful for traffic law enforcement in smart cities. The proposed method is applied on MIO-TCD vehicle dataset and EBVT video dataset. The results are calculated using three different metrics namely average accuracy, mean precision and mean recall. Obtained results are also compared with other state of the art methods. The results show significant improvement and thus the method can be effectively used for video analysis. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature. |
| Author Keywords | Deep learning; Intelligent transportation system; RCNN; Smart city; Vehicle detection |
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