Dr. ZHANG Yunping, SherrySherryDr. ZHANG YunpingMikolajczyk, K.K.Mikolajczyk2025-08-272025-08-272019Zhang, Y., & Mikolajczyk, K. (2019). Multi-block fusion for vehicle re-identification. In ICDP (Ed.). 9th International conference on imaging for crime detection and prevention (ICDP-2019). 9th International Conference on Imaging for Crime Detection and Prevention (ICDP-2019), London, UK (pp. 38-43). Institution of Engineering and Technology.9781839531095http://hdl.handle.net/20.500.11861/24685The extensive coverage of surveillance camera networks has supported the ever-growing research of vehicle re-identification (re-ID) due to their significant applications in matching and tracking vehicles-of-interest. The inherent challenging characteristics such as intra-class variance and inter-class similarity make the re-identification one of the most difficult tasks in computer vision. In this paper, we proposed a novel approach for vehicle re-id based on multi-block features. It implements the idea of information fusion from intermediate levels of representation and multi-stage supervision into a fully convolutional neural network. To demonstrate the effectiveness and superiority of our approach we perform extensive experiments and analysis on two standard vehicle re-id benchmarks.enVehicle Re-IdentificationConvolutional Neural NetworkComputer VisionInformation FusionIntra-Class VarianceSurveillance CamerasInterclass SimilarityDeep LearningImage SizeDeep Convolutional Neural NetworkBenchmark DatasetsGenerative Adversarial NetworksAverage PrecisionBackbone NetworkMean Average PrecisionSpatiotemporal InformationMetric LearningTriplet LossQuery ImageLicense PlateUnconstrained ConditionsDeep Metric LearningSoftplusVehicle ImagesVehicle IdentificationConference Paper10.1049/cp.2019.1165