  We are concerned with retrieving a query person from multiple videos captured
by a non-overlapping camera network. Existing methods often rely on purely
visual matching or consider temporal constraints but ignore the spatial
information of the camera network. To address this issue, we propose a
pedestrian retrieval framework based on cross-camera trajectory generation,
which integrates both temporal and spatial information. To obtain pedestrian
trajectories, we propose a novel cross-camera spatio-temporal model that
integrates pedestrians' walking habits and the path layout between cameras to
form a joint probability distribution. Such a spatio-temporal model among a
camera network can be specified using sparsely sampled pedestrian data. Based
on the spatio-temporal model, cross-camera trajectories can be extracted by the
conditional random field model and further optimized by restricted non-negative
matrix factorization. Finally, a trajectory re-ranking technique is proposed to
improve the pedestrian retrieval results. To verify the effectiveness of our
method, we construct the first cross-camera pedestrian trajectory dataset, the
Person Trajectory Dataset, in real surveillance scenarios. Extensive
experiments verify the effectiveness and robustness of the proposed method.
