A Two-Stage Approach for Bag Detection in Pedestrian Images
Bag detection in pedestrian images is a very practical visual surveillance problem. It is challenging because bag appearance may vary greatly. In this paper, we propose a novel two-stage approach for bag detection in pedestrian images. Firstly, we utilize two stripe vocabulary forests to check wheth...
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Published in | Computer Vision -- ACCV 2014 pp. 507 - 521 |
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Main Authors | , , |
Format | Book Chapter |
Language | English |
Published |
Cham
Springer International Publishing
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Series | Lecture Notes in Computer Science |
Subjects | |
Online Access | Get full text |
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Summary: | Bag detection in pedestrian images is a very practical visual surveillance problem. It is challenging because bag appearance may vary greatly. In this paper, we propose a novel two-stage approach for bag detection in pedestrian images. Firstly, we utilize two stripe vocabulary forests to check whether a pedestrian is with a bag. Secondly, we locate the bag location by ranking the generated bottom-up region proposals. The ranker is learned with a convolutional neural network (CNN). Experiments are performed on a subset of CUHK person re-identification dataset that show the effectiveness of our approach for bag detection in pedestrian images. Although developed for a specific problem, our approach could be applied to detect other carrying objects in pedestrian images. |
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ISBN: | 3319168169 9783319168166 |
ISSN: | 0302-9743 1611-3349 |
DOI: | 10.1007/978-3-319-16817-3_33 |