In the conventional person Re-ID setting, it is assumed that cropped images are the person images within the bounding box for each individual. However, in a crowded scene, off-shelf-detectors may generate bounding boxes involving multiple people, where the large proportion of background pedestrians or human occlusion exists. The representation extracted from such cropped images, which contain both the target and the interference pedestrians, might include distractive information. This will lead to wrong retrieval results. To address this problem, this paper presents a novel deep network termed Pedestrian-Interference Suppression Network (PISNet). PISNet leverages a Query-Guided Attention Block (QGAB) to enhance the feature of the target in the gallery, under the guidance of the query. Furthermore, the involving Guidance Reversed Attention Module and the Multi-Person Separation Loss promote QGAB to suppress the interference of other pedestrians. Our method is evaluated on two new pedestrian-interference datasets and the results show that the proposed method performs favorably against existing Re-ID methods.
CITATION STYLE
Zhao, S., Gao, C., Zhang, J., Cheng, H., Han, C., Jiang, X., … Sun, X. (2020). Do Not Disturb Me: Person Re-identification Under the Interference of Other Pedestrians. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12351 LNCS, pp. 647–663). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-58539-6_39
Mendeley helps you to discover research relevant for your work.