A Joint Spatiotemporal Prediction and Image Confirmation Model for Vehicle Trajectory Concatenation With Low Detection Rates

Ensuring the quality of trajectories is of utmost importance in traffic flow analysis. Traditional approaches rely on reconstructing nearly complete trajectories and subsequently denoising them. However, low detection rates often pose challenges and result in failed trajectory construction. To overcome this issue, this paper presents a trajectory concatenation method that combines NS Transformer prediction and Siamese-VGG16 similarity confirmation, specifically designed to address low detection rates. The employed transformer model can withstand missing values, efficiently extracting internal associations among multiple traffic parameters in conditions of sparse data. Furthermore, a lightweight image feature similarity verification step is integrated after trajectory prediction to find the most similar target to the image in the predicted spatiotemporal domain. Additionally, a lightweight image feature similarity verification step is integrated after trajectory prediction to identify the most similar targets within the predicted spatiotemporal domain. Experimental results demonstrate the efficacy of the proposed method, successfully connecting over 80% of fragmented tracks and yielding significant maintenance of MOTA above 0.74 under low detection accuracy.