A Unified Multi-modal Structure for Retrieving Tracked Vehicles through Natural Language Descriptions

Through the development of multi-modal and contrastive learning, image and video retrieval have made immense progress over the last years. Organically fused text, image, and video knowledge brings huge potential opportunities for multi-dimension, and multi-view retrieval, especially in traffic senses. This paper proposes a novel Multimodal Language Vehicle Retrieval (MLVR) system, for retrieving the trajectory of tracked vehicles based on natural language descriptions. The MLVR system is mainly combined with an end-to-end text-video contrastive learning model, a CLIP few-shot domain adaption method, and a semi-centralized control optimization system. Through a comprehensive understanding the knowledge from the vehicle type, color, maneuver, and surrounding environment, the MLVR forms a robust method to recognize an effective trajectory with provided natural language descriptions. Under this structure, our approach has achieved 81.79% Mean Reciprocal Rank (MRR) accuracy on the test dataset, in the 7th AI City Challenge Track 2, Tracked-Vehicle Retrieval by Natural Language Descriptions, rendering the 2nd rank on the public leaderboard. Our code is available at https://github.com/eadst/MLVR.