Enhancing Pedestrian Group Detection and Tracking Through Zone-Based Clustering
Advancements in self-driving car technology have the potential to revolutionize transportation by enhancing safety, efficiency, and accessibility. Nonetheless, the successful integration of autonomous vehicles into our urban landscapes necessitates robust and reliable pedestrian detection and tracki...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10328571/ |
| _version_ | 1797321075256721408 |
|---|---|
| author | Mingzuoyang Chen Shadi Banitaan Mina Maleki |
| author_facet | Mingzuoyang Chen Shadi Banitaan Mina Maleki |
| author_sort | Mingzuoyang Chen |
| collection | DOAJ |
| description | Advancements in self-driving car technology have the potential to revolutionize transportation by enhancing safety, efficiency, and accessibility. Nonetheless, the successful integration of autonomous vehicles into our urban landscapes necessitates robust and reliable pedestrian detection and tracking systems. As we frequently observe pedestrians moving together, tracking them as a group becomes a beneficial approach, mitigating occlusion and enhancing both the accuracy and speed of object detection and tracking. However, utilizing a human-view camera in an autonomous vehicle presents challenges as pedestrians occupy varied fields of view. In some instances, pedestrians closer to the camera may overlap with those farther away, as seen from the camera’s viewpoint, which causes the mis-groupings to happen. To address these challenges, we proposed a strategy to divide the image into distinct zones and perform grouping within each, significantly minimizing mis-groupings. First, an object detection method extracted pedestrians and their bounding boxes from an image. Second, zone detection was applied to separate the image into several zones. Third, clustering methods were applied to detect pedestrian groups within each zone. Last, the object tracking method was utilized to track pedestrian groups. We repeated the process over a ten-frame sequence to achieve better performance, with object detection executed in the first frame and object tracking in the remaining nine frames. The comparison of processing times of different group detection methods indicated that tracking pedestrian groups is more time-efficient than tracking individuals and achieved a 4.5% to 14.1% improvement. Furthermore, according to the Adjusted Rand Index (ARI) evaluation metric, our proposed zone-based group detection method outperforms the other commonly used approaches by achieving scores of 0.635 on the MOT17 dataset and 0.781 on the KITTI dataset. In addition, the proposed approach surpasses the other approaches in addressing scenarios where individuals from different fields of view intersect with each other. |
| first_indexed | 2024-03-08T04:52:19Z |
| format | Article |
| id | doaj.art-166a6a5e0e7b4389a7e7588ec9f65631 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T04:52:19Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-166a6a5e0e7b4389a7e7588ec9f656312024-02-08T00:01:22ZengIEEEIEEE Access2169-35362023-01-011113216213217910.1109/ACCESS.2023.333659210328571Enhancing Pedestrian Group Detection and Tracking Through Zone-Based ClusteringMingzuoyang Chen0https://orcid.org/0009-0009-0931-144XShadi Banitaan1https://orcid.org/0000-0003-2531-2505Mina Maleki2Department of Electrical and Computer Engineering, University of Detroit Mercy, Detroit, MI, USADepartment of Electrical and Computer Engineering, University of Detroit Mercy, Detroit, MI, USADepartment of Electrical and Computer Engineering, University of Detroit Mercy, Detroit, MI, USAAdvancements in self-driving car technology have the potential to revolutionize transportation by enhancing safety, efficiency, and accessibility. Nonetheless, the successful integration of autonomous vehicles into our urban landscapes necessitates robust and reliable pedestrian detection and tracking systems. As we frequently observe pedestrians moving together, tracking them as a group becomes a beneficial approach, mitigating occlusion and enhancing both the accuracy and speed of object detection and tracking. However, utilizing a human-view camera in an autonomous vehicle presents challenges as pedestrians occupy varied fields of view. In some instances, pedestrians closer to the camera may overlap with those farther away, as seen from the camera’s viewpoint, which causes the mis-groupings to happen. To address these challenges, we proposed a strategy to divide the image into distinct zones and perform grouping within each, significantly minimizing mis-groupings. First, an object detection method extracted pedestrians and their bounding boxes from an image. Second, zone detection was applied to separate the image into several zones. Third, clustering methods were applied to detect pedestrian groups within each zone. Last, the object tracking method was utilized to track pedestrian groups. We repeated the process over a ten-frame sequence to achieve better performance, with object detection executed in the first frame and object tracking in the remaining nine frames. The comparison of processing times of different group detection methods indicated that tracking pedestrian groups is more time-efficient than tracking individuals and achieved a 4.5% to 14.1% improvement. Furthermore, according to the Adjusted Rand Index (ARI) evaluation metric, our proposed zone-based group detection method outperforms the other commonly used approaches by achieving scores of 0.635 on the MOT17 dataset and 0.781 on the KITTI dataset. In addition, the proposed approach surpasses the other approaches in addressing scenarios where individuals from different fields of view intersect with each other.https://ieeexplore.ieee.org/document/10328571/DBSCANK-meansobject detectionobject trackingzone-based group detection |
| spellingShingle | Mingzuoyang Chen Shadi Banitaan Mina Maleki Enhancing Pedestrian Group Detection and Tracking Through Zone-Based Clustering IEEE Access DBSCAN K-means object detection object tracking zone-based group detection |
| title | Enhancing Pedestrian Group Detection and Tracking Through Zone-Based Clustering |
| title_full | Enhancing Pedestrian Group Detection and Tracking Through Zone-Based Clustering |
| title_fullStr | Enhancing Pedestrian Group Detection and Tracking Through Zone-Based Clustering |
| title_full_unstemmed | Enhancing Pedestrian Group Detection and Tracking Through Zone-Based Clustering |
| title_short | Enhancing Pedestrian Group Detection and Tracking Through Zone-Based Clustering |
| title_sort | enhancing pedestrian group detection and tracking through zone based clustering |
| topic | DBSCAN K-means object detection object tracking zone-based group detection |
| url | https://ieeexplore.ieee.org/document/10328571/ |
| work_keys_str_mv | AT mingzuoyangchen enhancingpedestriangroupdetectionandtrackingthroughzonebasedclustering AT shadibanitaan enhancingpedestriangroupdetectionandtrackingthroughzonebasedclustering AT minamaleki enhancingpedestriangroupdetectionandtrackingthroughzonebasedclustering |