基于步态的摄像机网络跨视域行人跟踪

Pedestrian tracking across non-overlapping camera views is one of the basic problems of intelligent visual surveillance. A cross-view pedestrian target tracking method based on the gait features of a 2D skeleton diagram and space-time constraints is proposed in order to address the issue that the pedestrian cross-view tracking method based on the assumption of appearance consistency is sensitive to lighting or clothing changes. The skeleton set is extracted from the local trajectory of the single view to calculate the gait features, and the integer programming model of the cross-view target tracking problem is established. The model parameters are defined by the similarity of the gait features and the space-time constraints. The dual decomposition algorithm is used to realize the distributed solution to the above problems. The algorithm’s robustness to changes in lighting and clothing is greatly increased through the combination of gait features and more precise space-time restrictions, and it also solves the issue of weak discriminating when gait or space-time features are employed alone. The test results on the public data sets show that the proposed method is accurate in tracking and robust to lighting and clothing changes.