Pedestrian- and Vehicle-Detection Algorithm Based on Improved Aggregated Channel Features
In advanced driver-assistance systems (ADAS), the accuracy and real-time performance of pedestrian- and vehicle-detection algorithms based on vision sensors are crucial for safety. Here, a lightweight detection algorithm based on aggregated channel features (ACFs),consisting of a context pixel ACF (...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9349435/ |
| _version_ | 1811210296770953216 |
|---|---|
| author | Jie Hua Ying Shi Changjun Xie Hui Zhang Jian Zhang |
| author_facet | Jie Hua Ying Shi Changjun Xie Hui Zhang Jian Zhang |
| author_sort | Jie Hua |
| collection | DOAJ |
| description | In advanced driver-assistance systems (ADAS), the accuracy and real-time performance of pedestrian- and vehicle-detection algorithms based on vision sensors are crucial for safety. Here, a lightweight detection algorithm based on aggregated channel features (ACFs),consisting of a context pixel ACF (CP-ACF) pedestrian detector and a multiview ACF (Mv-ACF) vehicle detector, is proposed to rapidly and precisely understand road scenes. The former fuses local and context information to improve the robustness to pedestrian deformation, while the latter contains a number of subclass detectors to alleviate intraclass differences due to different viewing angles. Compared to the original ACF, the CP-ACF pedestrian detector reduces the average miss rate (AMR) by 6.34%. The Mv-ACF vehicle detector improves the average precision (AP) by 40.26% on average at easy, moderate and hard levels. This remarkable effectiveness is due to the spectrum clustering of multiview samples and the resulting integration of these subclass detectors via confidence score calibration, which reduces the intraclass differences of vehicles. Since feature extraction takes up 68.8% of the total detection time, a mechanism of feature sharing between pedestrian and vehicle detectors is advanced to reduce the time spent in feature extraction. A strategy based on ground-plane constraints (GPCs) is proposed to control false detection of pedestrians and vehicles by incorporating road prior information, which reduces the AMR by 1.07% for CP-ACF pedestrian detectors and improves the AP by 0.27% on average for Mv-ACF vehicle detectors. Thus, the proposed algorithm can effectively control false detection by road prior information. |
| first_indexed | 2024-04-12T04:53:18Z |
| format | Article |
| id | doaj.art-35e1b77ad5324cb7851224cfcf057f1c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T04:53:18Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-35e1b77ad5324cb7851224cfcf057f1c2022-12-22T03:47:14ZengIEEEIEEE Access2169-35362021-01-019258852589710.1109/ACCESS.2021.30576169349435Pedestrian- and Vehicle-Detection Algorithm Based on Improved Aggregated Channel FeaturesJie Hua0https://orcid.org/0000-0002-7721-5240Ying Shi1Changjun Xie2https://orcid.org/0000-0002-9626-0813Hui Zhang3https://orcid.org/0000-0003-4206-4339Jian Zhang4School of Automation, Wuhan University of Technology, Wuhan, ChinaSchool of Automation, Wuhan University of Technology, Wuhan, ChinaSchool of Automation, Wuhan University of Technology, Wuhan, ChinaIntelligent Transportation Systems Research Center (ITSC), Wuhan University of Technology, Wuhan, ChinaSchool of Automation, Wuhan University of Technology, Wuhan, ChinaIn advanced driver-assistance systems (ADAS), the accuracy and real-time performance of pedestrian- and vehicle-detection algorithms based on vision sensors are crucial for safety. Here, a lightweight detection algorithm based on aggregated channel features (ACFs),consisting of a context pixel ACF (CP-ACF) pedestrian detector and a multiview ACF (Mv-ACF) vehicle detector, is proposed to rapidly and precisely understand road scenes. The former fuses local and context information to improve the robustness to pedestrian deformation, while the latter contains a number of subclass detectors to alleviate intraclass differences due to different viewing angles. Compared to the original ACF, the CP-ACF pedestrian detector reduces the average miss rate (AMR) by 6.34%. The Mv-ACF vehicle detector improves the average precision (AP) by 40.26% on average at easy, moderate and hard levels. This remarkable effectiveness is due to the spectrum clustering of multiview samples and the resulting integration of these subclass detectors via confidence score calibration, which reduces the intraclass differences of vehicles. Since feature extraction takes up 68.8% of the total detection time, a mechanism of feature sharing between pedestrian and vehicle detectors is advanced to reduce the time spent in feature extraction. A strategy based on ground-plane constraints (GPCs) is proposed to control false detection of pedestrians and vehicles by incorporating road prior information, which reduces the AMR by 1.07% for CP-ACF pedestrian detectors and improves the AP by 0.27% on average for Mv-ACF vehicle detectors. Thus, the proposed algorithm can effectively control false detection by road prior information.https://ieeexplore.ieee.org/document/9349435/Pedestrian and vehicle detectionACFanti-deformationmultiviewground plane constraintlightweight |
| spellingShingle | Jie Hua Ying Shi Changjun Xie Hui Zhang Jian Zhang Pedestrian- and Vehicle-Detection Algorithm Based on Improved Aggregated Channel Features IEEE Access Pedestrian and vehicle detection ACF anti-deformation multiview ground plane constraint lightweight |
| title | Pedestrian- and Vehicle-Detection Algorithm Based on Improved Aggregated Channel Features |
| title_full | Pedestrian- and Vehicle-Detection Algorithm Based on Improved Aggregated Channel Features |
| title_fullStr | Pedestrian- and Vehicle-Detection Algorithm Based on Improved Aggregated Channel Features |
| title_full_unstemmed | Pedestrian- and Vehicle-Detection Algorithm Based on Improved Aggregated Channel Features |
| title_short | Pedestrian- and Vehicle-Detection Algorithm Based on Improved Aggregated Channel Features |
| title_sort | pedestrian and vehicle detection algorithm based on improved aggregated channel features |
| topic | Pedestrian and vehicle detection ACF anti-deformation multiview ground plane constraint lightweight |
| url | https://ieeexplore.ieee.org/document/9349435/ |
| work_keys_str_mv | AT jiehua pedestrianandvehicledetectionalgorithmbasedonimprovedaggregatedchannelfeatures AT yingshi pedestrianandvehicledetectionalgorithmbasedonimprovedaggregatedchannelfeatures AT changjunxie pedestrianandvehicledetectionalgorithmbasedonimprovedaggregatedchannelfeatures AT huizhang pedestrianandvehicledetectionalgorithmbasedonimprovedaggregatedchannelfeatures AT jianzhang pedestrianandvehicledetectionalgorithmbasedonimprovedaggregatedchannelfeatures |