Multi-label learning with multi-label smoothing regularization for vehicle re-identification

Abstract

Vehicle re-identification (re-ID) is a vital technique to the urban intelligent video surveillance system and smart city. Given a query vehicle image, the vehicle re-ID aims to search and retrieve the images of the same vehicle that have been captured by different surveillance cameras with various viewing angles. Based on the observation that essential vehicle attributes, like vehicle‘s color and types (e.g., sedan, bus, truck, and so on), could be used as important traits to recognize vehicle, an effective multi-label learning (MLL) method is proposed in this paper that can simultaneously learn three labels: vehicle’s ID, type, and color. With three labels, a multi-label smoothing regularization (MLSR) is further proposed, which can allocate a uniform label distribution to the multi-labeled training images to regularize MLL model and improve vehicle re-ID performance. Extensive experiments conducted on the VeRi and VehicleID datasets have demonstrated that the proposed MLL with MLSR approach can effectively improve the performance delivered by the baseline and outperform multiple state-of-the-art vehicle re-ID methods as well.