LAUSR

In recent years, unmanned aerial vehicles (UAVs) is widely used in vehicle tracking. However, the objects in drone pictures always consist of fewer pixels due to their flying height. The lacking of visual information results in unreliable tracking results. Meanwhile, higher flying heights capture more targets, which makes it difficult to perform real-time inference with existing methods. To solve the above problems, we present a lightweight tracking model which employed behavioral information in the multivehicle tracking (MVT) problem. Besides, BVTracker utilizes visual information as well as behavioral information independently and consists of two branches. One is a trajectory prediction model based on long short-term memory (LSTM) network and self-attention network. The other one is an association model base on the Hungarian Algorithm. To enable MVT, the trajectory of each vehicle is predicted by the trajectory prediction model and the prediction results are associated with the detection findings. Moreover, a new dataset collected by UAVs for vehicles on freeways is applied to train and validate BVTracker. Compared with the state-of-art tracking algorithms, the experimental results show that the proposed method is superior to the existing algorithms at different frame rates and can achieve both robustness and real-time requirements, which profits the fast and effective traffic data analysis.