  Vehicle re-identification (ReID) is a computer vision task that matches the
same vehicle across different cameras or viewpoints in a surveillance system.
This is crucial for Intelligent Transportation Systems (ITS), where the
effectiveness is influenced by the regions from which vehicle images are
cropped. This study explores whether optimal vehicle detection regions, guided
by detection confidence scores, can enhance feature matching and ReID tasks.
Using our framework with multiple Regions of Interest (ROIs) and lane-wise
vehicle counts, we employed YOLOv8 for detection and DeepSORT for tracking
across twelve Indiana Highway videos, including two pairs of videos from
non-overlapping cameras. Tracked vehicle images were cropped from inside and
outside the ROIs at five-frame intervals. Features were extracted using
pre-trained models: ResNet50, ResNeXt50, Vision Transformer, and
Swin-Transformer. Feature consistency was assessed through cosine similarity,
information entropy, and clustering variance. Results showed that features from
images cropped inside ROIs had higher mean cosine similarity values compared to
those involving one image inside and one outside the ROIs. The most significant
difference was observed during night conditions (0.7842 inside vs. 0.5 outside
the ROI with Swin-Transformer) and in cross-camera scenarios (0.75
inside-inside vs. 0.52 inside-outside the ROI with Vision Transformer).
Information entropy and clustering variance further supported that features in
ROIs are more consistent. These findings suggest that strategically selected
ROIs can enhance tracking performance and ReID accuracy in ITS.
