[PDF][PDF] Robust pedestrian detection and path prediction using improved yolov5
KO Hajari, U Gawande, Y Golhar - ELCVIA Electronic Letters on …, 2022 - elcvia.cvc.uab.cat
KO Hajari, U Gawande, Y Golhar
ELCVIA Electronic Letters on Computer Vision and Image Analysis, 2022•elcvia.cvc.uab.cat… Occupied pedestrians are detected on multi-scales using the improved YOLOv5 model.
We improved the YOLOv5 detection method in three ways: 1) a new feature fusion layer has
been added to capture more shallow feature information of small size pedestrians, 2)
features from the backbone network have been brought into the feature fusion layers to
reduce feature information loss of small size pedestrians; and 3) Scale Invariant Cross-stage
Partial Network (SCSP) has been added to detect the pose and scale invariant pedestrians …
We improved the YOLOv5 detection method in three ways: 1) a new feature fusion layer has
been added to capture more shallow feature information of small size pedestrians, 2)
features from the backbone network have been brought into the feature fusion layers to
reduce feature information loss of small size pedestrians; and 3) Scale Invariant Cross-stage
Partial Network (SCSP) has been added to detect the pose and scale invariant pedestrians …
Abstract
In vision-based surveillance systems, pedestrian recognition and path prediction are critical concerns. Advanced computer vision applications, on the other hand, confront numerous challenges
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