Inception Convolution and Feature Fusion for Person Search
With the rapid advancement of deep learning theory and hardware device computing capacity, computer vision tasks, such as object detection and instance segmentation, have entered a revolutionary phase in recent years. As a result, extremely challenging integrated tasks, such as person search, might...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/4/1984 |
| _version_ | 1797618253065879552 |
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| author | Huan Ouyang Jiexian Zeng Lu Leng |
| author_facet | Huan Ouyang Jiexian Zeng Lu Leng |
| author_sort | Huan Ouyang |
| collection | DOAJ |
| description | With the rapid advancement of deep learning theory and hardware device computing capacity, computer vision tasks, such as object detection and instance segmentation, have entered a revolutionary phase in recent years. As a result, extremely challenging integrated tasks, such as person search, might develop quickly. The majority of efficient network frameworks, such as Seq-Net, are based on Faster R-CNN. However, because of the parallel structure of Faster R-CNN, the performance of re-ID can be significantly impacted by the single-layer, low resolution, and occasionally overlooked check feature diagrams retrieved during pedestrian detection. To address these issues, this paper proposed a person search methodology based on an inception convolution and feature fusion module (IC-FFM) using Seq-Net (Sequential End-to-end Network) as the benchmark. First, we replaced the general convolution in ResNet-50 with the new inception convolution module (ICM), allowing the convolution operation to effectively and dynamically distribute various channels. Then, to improve the accuracy of information extraction, the feature fusion module (FFM) was created to combine multi-level information using various levels of convolution. Finally, Bounding Box regression was created using convolution and the double-head module (DHM), which considerably enhanced the accuracy of pedestrian retrieval by combining global and fine-grained information. Experiments on CHUK-SYSU and PRW datasets showed that our method has higher accuracy than Seq-Net. In addition, our method is simpler and can be easily integrated into existing two-stage frameworks. |
| first_indexed | 2024-03-11T08:10:29Z |
| format | Article |
| id | doaj.art-0f58c3a335e740ac9ba89458205a8bd6 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T08:10:29Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0f58c3a335e740ac9ba89458205a8bd62023-11-16T23:08:44ZengMDPI AGSensors1424-82202023-02-01234198410.3390/s23041984Inception Convolution and Feature Fusion for Person SearchHuan Ouyang0Jiexian Zeng1Lu Leng2School of Software, Nanchang Hangkong University, Nanchang 330063, ChinaSchool of Software, Nanchang Hangkong University, Nanchang 330063, ChinaSchool of Software, Nanchang Hangkong University, Nanchang 330063, ChinaWith the rapid advancement of deep learning theory and hardware device computing capacity, computer vision tasks, such as object detection and instance segmentation, have entered a revolutionary phase in recent years. As a result, extremely challenging integrated tasks, such as person search, might develop quickly. The majority of efficient network frameworks, such as Seq-Net, are based on Faster R-CNN. However, because of the parallel structure of Faster R-CNN, the performance of re-ID can be significantly impacted by the single-layer, low resolution, and occasionally overlooked check feature diagrams retrieved during pedestrian detection. To address these issues, this paper proposed a person search methodology based on an inception convolution and feature fusion module (IC-FFM) using Seq-Net (Sequential End-to-end Network) as the benchmark. First, we replaced the general convolution in ResNet-50 with the new inception convolution module (ICM), allowing the convolution operation to effectively and dynamically distribute various channels. Then, to improve the accuracy of information extraction, the feature fusion module (FFM) was created to combine multi-level information using various levels of convolution. Finally, Bounding Box regression was created using convolution and the double-head module (DHM), which considerably enhanced the accuracy of pedestrian retrieval by combining global and fine-grained information. Experiments on CHUK-SYSU and PRW datasets showed that our method has higher accuracy than Seq-Net. In addition, our method is simpler and can be easily integrated into existing two-stage frameworks.https://www.mdpi.com/1424-8220/23/4/1984person searchFaster R-CNNinception convolutionfeature fusionregion proposal network (RPN)double-head |
| spellingShingle | Huan Ouyang Jiexian Zeng Lu Leng Inception Convolution and Feature Fusion for Person Search Sensors person search Faster R-CNN inception convolution feature fusion region proposal network (RPN) double-head |
| title | Inception Convolution and Feature Fusion for Person Search |
| title_full | Inception Convolution and Feature Fusion for Person Search |
| title_fullStr | Inception Convolution and Feature Fusion for Person Search |
| title_full_unstemmed | Inception Convolution and Feature Fusion for Person Search |
| title_short | Inception Convolution and Feature Fusion for Person Search |
| title_sort | inception convolution and feature fusion for person search |
| topic | person search Faster R-CNN inception convolution feature fusion region proposal network (RPN) double-head |
| url | https://www.mdpi.com/1424-8220/23/4/1984 |
| work_keys_str_mv | AT huanouyang inceptionconvolutionandfeaturefusionforpersonsearch AT jiexianzeng inceptionconvolutionandfeaturefusionforpersonsearch AT luleng inceptionconvolutionandfeaturefusionforpersonsearch |