Person Search via Deep Integrated Networks
| Autor: | Cheng-Rong Lin, Ju-Chin Chen, Cheng-Feng Wu, Chun-Huei Chen |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Computer science
person search ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology Pedestrian Variation (game tree) lcsh:Technology Image (mathematics) lcsh:Chemistry Discriminative model Bounding overwatch Minimum bounding box 020204 information systems 0202 electrical engineering electronic engineering information engineering General Materials Science Computer vision Instrumentation lcsh:QH301-705.5 cnn Fluid Flow and Transfer Processes person re-identification business.industry lcsh:T Process Chemistry and Technology General Engineering lcsh:QC1-999 Computer Science Applications Identification (information) lcsh:Biology (General) lcsh:QD1-999 lcsh:TA1-2040 Clutter 020201 artificial intelligence & image processing Artificial intelligence business lcsh:Engineering (General). Civil engineering (General) lcsh:Physics |
| Zdroj: | Applied Sciences, Vol 10, Iss 1, p 188 (2019) Applied Sciences Volume 10 Issue 1 |
| ISSN: | 2076-3417 |
| Popis: | This study proposes an integrated deep network consisting of a detection and identification module for person search. Person search is a very challenging problem because of the large appearance variation caused by occlusion, background clutter, pose variations, etc., and it is still an active research issue in the academic and industrial fields. Although various studies have been proposed, following the protocols of the person re-identification (ReID) benchmarks, most existing works take cropped pedestrian images either from manual labelling or a perfect detection assumption. However, for person search, manual processing is unavailable in practical applications, thereby causing a gap between the ReID problem setting and practical applications. One fact is also ignored: an imperfect auto-detected bounding box or misalignment is inevitable. We design herein a framework for the practical surveillance scenarios in which the scene images are captured. For person search, detection is a necessary step before ReID, and previous studies have shown that the precision of detection results has an influence on person ReID. The detection module based on the Faster R-CNN is used to detect persons in a scene image. For identifying and extracting discriminative features, a multi-class CNN network is trained with the auto-detected bounding boxes from the detection module, instead of the manually cropped data. The distance metric is then learned from the discriminative features output by the identification module. According to the experimental results of the test performed in the scene images, the multi-class CNN network for the identification module can provide a 62.7% accuracy rate, which is higher than that for the two-class CNN network. |
| Databáze: | OpenAIRE |
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