Aggregating Global and Local Visual Representation for Vehicle Re-IDentification

Vehicle Re-Identification targets at searching for vehicle instances of the same identity with a given query. It has gained an increasing attention recently with a wide application prospects in video surveillance and intelligent transportation. The main challenge lies in how to distinguish the subtle differences between different vehicles, while capturing the slight similarity between the instance of the same vehicle in different viewpoints or illuminations. To this end, most existing methods focus on learning discriminative global representations, which leave the unique local details such as stickers, inspection labels and driver wearing being unexploited. In this paper, we present a novel coarse-to-fine scheme that aggregates global and local visual representations to boost the retrieval accuracy. Specifically, a multi-task learning framework combining an Attribute Learning branch and a Deep Ranking branch (termed ALDR) is first adopted to learn robust global features, which produces an initial ranking list. Then the local similarities between image patches in the initial ranking list and in the query is computed via a Multi-Channel and Multi-Scale Siamese network (termed MCMS-Siam). Finally, the retrieval result is returned after re-ranking the initial list according to such a combination of global and local similarities. Experimental results on the widely-used VehicleID dataset and VECH-WILD dataset demonstrate the merits of the proposed method over the state-of-the-art methods.