Trajectory recovery based on the snapshots from the city-wide multi-camera
network facilitates urban mobility sensing and driveway optimization. The
state-of-the-art solutions devoted to such a vision-based scheme typically
incorporate predefined rules or unsupervised iterative feedback, struggling
with multi-fold challenges such as lack of open-source datasets for training
the whole pipeline, and the vulnerability to the noises from visual inputs. In
response to the dilemma, this paper proposes VisionTraj, the first
learning-based model that reconstructs vehicle trajectories from snapshots
recorded by road network cameras. Coupled with it, we elaborate on two rational
vision-trajectory datasets, which produce extensive trajectory data along with
corresponding visual snapshots, enabling supervised vision-trajectory interplay
extraction. Following the data creation, based on the results from the
off-the-shelf multi-modal vehicle clustering, we first re-formulate the
trajectory recovery problem as a generative task and introduce the canonical
Transformer as the autoregressive backbone. Then, to identify clustering noises
(e.g., false positives) with the bound on the snapshots' spatiotemporal
dependencies, a GCN-based soft-denoising module is conducted based on the fine-
and coarse-grained Re-ID clusters. Additionally, we harness strong semantic
information extracted from the tracklet to provide detailed insights into the
vehicle's entry and exit actions during trajectory recovery. The denoising and
tracklet components can also act as plug-and-play modules to boost baselines.
Experimental results on the two hand-crafted datasets show that the proposed
VisionTraj achieves a maximum +11.5% improvement against the sub-best model.

本文提出了 VisionTraj，一种基于学习的模型，可以从道路网络摄像机记录的快照重建车辆轨迹，并通过引入神经网络和 GCN-based 软去噪模块以及轨迹信息提高建模效果。实验结果显示，VisionTraj 相较于其他模型有最大 11.5％的改进。