This research introduces a novel approach to vehicle re-identification across surveillance systems without requiring additional manual annotations.

• Leverages CLIP model for extracting semantic features from vehicle images
• Enables more accurate tracking of vehicles across different camera views
• Improves surveillance capabilities while reducing annotation costs
• Enhances security applications in intelligent transportation systems

For security professionals, this advancement means more reliable vehicle tracking in surveillance networks with reduced implementation complexity and data preparation requirements.

CLIP-SENet: CLIP-based Semantic Enhancement Network for Vehicle Re-identification