PEET: Prototype Embedding and Embedding Transition for Matching Vehicles over Disparate Viewpoints

This paper presents a novel framework, prototype embedding and embedding transition (PEET), for matching objects, especially vehicles, that undergo drastic pose, appearance, and even modality changes. The problem of matching objects seen under drastic variations is reduced to matching embeddings of object appearances instead of matching the object images directly. An object appearance is first embedded in the space of a representative set of model prototypes (prototype embedding (PE)). Objects captured at disparate temporal and spatial sites are embedded in the space of prototypes that are rendered with the pose of the cameras at the respective sites. Low dimensional embedding vectors are subsequently matched. A significant feature of our approach is that no mapping function is needed to compute the distance between embedding vectors extracted from objects viewed from disparate pose and appearance changes, instead, an embedding transition (ET) scheme is utilized to implicitly realize the complex and non-linear mapping with high accuracy. The heterogeneous nature of matching between high-resolution and low-resolution image objects in PEET is discussed, and an unsupervised learning scheme based on the exploitation of the heterogeneous nature is developed to improve the overall matching performance of mixed resolution objects. The proposed approach has been applied to vehicular object classification and query application, and the extensive experimental results demonstrate the efficacy and versatility of the PEET framework.