Vehicle Tracking Using Surveillance with Multimodal Data Fusion

Abstract--Vehicle location prediction or vehicle tracking is a significant topic within connected vehicles. This task, however, is difficult if only a single modal data is available, probably causing bias and impeding the accuracy. With the development of sensor networks in connected vehicles, multimodal data are becoming accessible. Therefore, we propose a framework for vehicle tracking with multimodal data fusion. Images, being processed in the module of vehicle detection, provide direct information about the features of vehicles, whereas velocity estimation can further evaluate the possible location of the target vehicles, which reduces the number of features being compared, and decreases the time consumption and computational cost. Vehicle detection is designed with a color-faster R-CNN, which takes both the shape and color of the vehicles into consideration. Meanwhile, velocity estimation is through the Kalman filter, which is a classical method for tracking. Finally, a multimodal data fusion method is applied to integrate these outcomes so that vehicle-tracking tasks can be achieved. Experimental results suggest the efficiency of our methods, which can track vehicles using a series of surveillance cameras in urban areas. ITH technological advancements in vehicles and transportation system, motorists require comfort and intelligent driving, not only mobility. Thus, there has been a great deal of research which mainly falls into one of two directions. On one hand, researchers tend to develop more intelligent vehicles, or devices that can be attached to vehicles, bringing up several popular topics such as autonomous vehicles or driverless vehicles [1].