CLIPVehicle: A Unified Framework for Vision-based Vehicle Search

2025-08-06

AI Summary

To address low vehicle search efficiency in video surveillance, high computational overhead of two-stage detection-and-re-identification pipelines, and the inherent objective conflict between detection (emphasizing shared structural patterns) and re-identification (focusing on discriminative individual traits) in joint modeling, this paper proposes the first end-to-end unified framework for joint vehicle detection and re-identification. Key innovations include: (1) a dual-granularity semantic region alignment module that explicitly enforces consistency between local part-level and global structural representations; (2) a multi-level identity learning strategy integrating global, instance-level, and feature-level discriminative embeddings; and (3) integration of vision-language models to enhance fine-grained semantic understanding and cross-modal alignment. Extensive experiments demonstrate significant improvements over state-of-the-art methods on our real-world CityFlowVS dataset and synthetic benchmarks. The framework also achieves superior performance on both vehicle re-identification and person search tasks.

Technology Category

• Computer Vision: Language and Vision
• Search and Optimization: Learning to Search
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Vehicles, as one of the most common and significant objects in the real world, the researches on which using computer vision technologies have made remarkable progress, such as vehicle detection, vehicle re-identification, etc. To search an interested vehicle from the surveillance videos, existing methods first pre-detect and store all vehicle patches, and then apply vehicle re-identification models, which is resource-intensive and not very practical. In this work, we aim to achieve the joint detection and re-identification for vehicle search. However, the conflicting objectives between detection that focuses on shared vehicle commonness and re-identification that focuses on individual vehicle uniqueness make it challenging for a model to learn in an end-to-end system. For this problem, we propose a new unified framework, namely CLIPVehicle, which contains a dual-granularity semantic-region alignment module to leverage the VLMs (Vision-Language Models) for vehicle discrimination modeling, and a multi-level vehicle identification learning strategy to learn the identity representation from global, instance and feature levels. We also construct a new benchmark, including a real-world dataset CityFlowVS, and two synthetic datasets SynVS-Day and SynVS-All, for vehicle search. Extensive experimental results demonstrate that our method outperforms the state-of-the-art methods of both vehicle Re-ID and person search tasks.

Problem

• Achieve joint detection and re-identification for vehicle search
• Resolve conflicting objectives between detection and re-identification
• Leverage VLMs for vehicle discrimination modeling

Innovation

• Dual-granularity semantic-region alignment module
• Multi-level vehicle identification learning strategy
• VLMs for vehicle discrimination modeling