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. Specifically, we fuse the results of two
modalities, images and velocity, in our vehicle-tracking task. 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.

本论文提出了一种基于多模态数据融合的车辆跟踪框架，该框架利用图像处理和速度估计的方法对城市区域中的车辆进行跟踪，并通过 Kalman 滤波等方法实现车辆定位和轨迹跟踪的任务。实验结果表明了该方法的高效性。