A real-time multi-vehicle tracking framework in intelligent vehicular networks

In this paper, we provide a new approach for intelligent traffic transportation in the intelligent vehicular networks, which aims at collecting the vehicles' locations, trajectories and other key driving parameters for the time-critical autonomous driving's requirement. The key of our method is a multi-vehicle tracking framework in the traffic monitoring scenario. Our proposed framework is composed of three modules: multi-vehicle detection, multi-vehicle association and miss-detected vehicle tracking. For the first module, we integrate self-attention mechanism into detector of using key point estimation for better detection effect. For the second module, we apply the multi-dimensional information for robustness promotion, including vehicle re-identification (Re-ID) features, historical trajectory information, and spatial position information For the third module, we re-track the miss-detected vehicles with occlusions in the first detection module. Besides, we utilize the asymmetric convolution and depth-wise separable convolution to reduce the model's parameters for speed-up. Extensive experimental results show the effectiveness of our proposed multi-vehicle tracking framework.