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.

智能交通系统（ITS）是信息和通信技术与交通控制和管理中重要应用程序的集成。城市地区道路车辆的数量增加，迫切需要开发交通管理自动化方法。车辆识别，分类和分析使智能交通系统能够做出决策。在本文中，提出了一种使用基于区域的卷积神经网络（RCNN）进行车辆识别的视频分析自动方法。在给定的帧中，分析安装在道路上的CCTV摄像机收集的交通视频，以识别车辆。经过预训练的Google网络用于提取功能。基于区域的卷积神经网络将这些功能用于车辆识别。使用使用对象交点（IoU）计算的概率分数来识别车辆。识别出的车辆分为十种不同的车辆类别。所提出的网络将来自先前各层的特征串联起来，以减少损失并因此提高车辆识别的准确性。车辆识别方法进一步扩展到车辆计数和行为分析。车辆计数信息可用于智慧城市中的拥堵控制。行为分析包括车辆速度的计算。速度信息对于智慧城市中的交通执法非常有用。该方法应用于MIO-TCD车辆数据集和EBVT视频数据集。使用三种不同的指标来计算结果，即平均准确度，平均准确度和平均召回率。还将获得的结果与其他现有技术方法进行比较。结果显示出显着的改进，因此该方法可以有效地用于视频分析。