Matching Cost Research Articles

The Adversarial Robust and Generalizable Stereo Matching for Infrared Binocular Based on Deep Learning

Despite the considerable success of deep learning methods in stereo matching for binocular images, the generalizability and robustness of these algorithms, particularly under challenging conditions such as occlusions or degraded infrared textures, remain uncertain. This paper presents a novel deep-learning-based depth optimization method that obviates the need for large infrared image datasets and adapts seamlessly to any specific infrared camera. Moreover, this adaptability extends to standard binocular images, allowing the method to work effectively on both infrared and visible light stereo images. We further investigate the role of infrared textures in a deep learning framework, demonstrating their continued utility for stereo matching even in complex lighting environments. To compute the matching cost volume, we apply the multi-scale census transform to the input stereo images. A stacked sand leak subnetwork is subsequently employed to address the matching task. Our approach substantially improves adversarial robustness while maintaining accuracy on comparison with state-of-the-art methods which decrease nearly a half in EPE for quantitative results on widely used autonomous driving datasets. Furthermore, the proposed method exhibits superior generalization capabilities, transitioning from simulated datasets to real-world datasets without the need for fine-tuning.

Pixel-wise matching cost function for robust light field depth estimation

Depth estimation has significant potential in industrial applications, such as robot navigation. Light Field (LF) technology captures both spatial and angular information of a scene, enabling precise depth acquisition. Stereo matching technology has been commonly used for this task in LF imaging. However, previous methods typically compute the matching cost by simply shifting images with predefined offsets, causing poor performance in regions with sudden depth distribution changes (e.g., edges). To address this issue, we propose a Pixel-wise Matching Cost Function (PMCF) to estimate depth in units of pixel, then design a pixel-based network, termed PixelNet, for depth prediction, which achieves top rankings on the HCI 4D light field benchmark. Specifically, our cost function consists of two modules: the range search strategy and the modulation mechanism. The range search strategy enables the network to iteratively optimize depth on a per-pixel basis, while the modulation mechanism effectively handles scene noise and occlusions. By integrating these two modules, our function enables the generation of depth maps with sharper edges and smoother surfaces, compared with previous methods. Finally, we design a pixel-based network and apply it to both synthetic and real-world scenes, demonstrating that our method outperforms state-of-the-art methods in both scenes. Furthermore, PixelNet can also function as a post-processing network that can be seamlessly integrated with existing methods for depth refinement.

Improvement of horizontal streak on disparity map thru parameter optimization for stereo vision algorithm

In this paper, an improved local based stereo vision disparity map (SVDM) algorithm is proposed. The proposed local based SVDM algorithm include four stages and they are matching cost computation, cost aggregation disparity optimization and disparity refinement. The matching cost computation started by combining pixel to pixel matching techniques, which are absolute difference (AD) and gradient matching (GM) in producing the initial disparity map. Next, the cost aggregation uses minimum spanning tree (MST) segmentation, which equipped with edge preserving properties and noise filtering. Then, disparity optimization uses local approach with winner-take-all (WTA) technique. At the final stage, disparity refinement uses bilateral filter (BF) with weighted median (WM), which can improve the disparity map through noise removing and edges preserving. Then, the research continues to optimize the proposed local based SVDM algorithm through parameters optimization in obtaining the final disparity map. Here, multiple parameters from the proposed SVDM algorithm are manipulated and they are constant values for GM and several constant parameters in BF. By selecting the optimum parameter values, the performance of the proposed SVDM algorithm increased, especially robustness towards the horizontal streaks.

Efficiency-Accuracy Trade-Off in Light Field Estimation with Cost Volume Construction and Aggregation.

The Rich spatial and angular information in light field images enables accurate depth estimation, which is a crucial aspect of environmental perception. However, the abundance of light field information also leads to high computational costs and memory pressure. Typically, selectively pruning some light field information can significantly improve computational efficiency but at the expense of reduced depth estimation accuracy in the pruned model, especially in low-texture regions and occluded areas where angular diversity is reduced. In this study, we propose a lightweight disparity estimation model that balances speed and accuracy and enhances depth estimation accuracy in textureless regions. We combined cost matching methods based on absolute difference and correlation to construct cost volumes, improving both accuracy and robustness. Additionally, we developed a multi-scale disparity cost fusion architecture, employing 3D convolutions and a UNet-like structure to handle matching costs at different depth scales. This method effectively integrates information across scales, utilizing the UNet structure for efficient fusion and completion of cost volumes, thus yielding more precise depth maps. Extensive testing shows that our method achieves computational efficiency on par with the most efficient existing methods, yet with double the accuracy. Moreover, our approach achieves comparable accuracy to the current highest-accuracy methods but with an order of magnitude improvement in computational performance.

Stereo matching algorithm using deep learning and edge-preserving filter for machine vision

Machine vision research began with a single-camera system, but these systems had various limitations from having just one point-of-view of the environment and no depth information, therefore stereo cameras were invented. This paper proposes a hybrid method of a stereo matching algorithm with the goal of generating an accurate disparity map critical for applications such as 3D surface reconstruction and robot navigation to name a few. Convolutional neural network (CNN) is utilised to generate the matching cost, which is then input into cost aggregation to increase accuracy with the help of a bilateral filter (BF). Winner-take-all (WTA) is used to generate the preliminary disparity map. An edge-preserving filter (EPF) is applied to that output based on a transform that defines an isometry between curves on the 2D image manifold in 5D and the real line to eliminate these artefacts. The transform warps the input signal adaptively to allow linear 1D filtering. Due to the filter's resistance to high contrast and brightness, it is effective in refining and removing noise from the output image. Based on experimental research employing a Middlebury standard validation benchmark, this approach gives high accuracy with an average non-occluded error of 6.71% comparable to other published methods.

Refining disparity maps using deep learning and edge-aware smoothing filter

Stereo matching algorithm is crucial for applications that rely on three-dimensional (3D) surface reconstruction, producing a disparity map that contains depth information by computing the disparity values between corresponding points from a stereo image pair. In order to yield desirable results, the proposed stereo matching algorithm must possess a high degree of resilience against radiometric variation and edge inconsistencies. In this article convolutional neural network (CNN) is employed in the first stage to generate the raw matching cost, which is subsequently filtered with a bilateral filter (BF) and applied with cross-based cost aggregation (CBCA) during the cost aggregation stage to enhance precision. Winner-take-all (WTA) strategy is implemented to normalise the disparity map values. Finally, the resulting output is subjected to an edge-aware smoothing filter (EASF) to reduce the noise. Due to its resistance to high contrast and brightness, the filter is found to be effective in refining and eliminating noise from the output image. Despite discontinuities like adiron's lost cup handle or artl's shattered rods, this approach, based on experimental research utilizing a Middlebury standard validation benchmark, yields a high level of accuracy, with an average non-occluded error of 6.79%, comparable to other published methods.

Dynamic microservice placement in multi-tier Fog networks

Fog computing extends Cloud-like infrastructure to the Edge, advancing Industrial Internet-of-Things (IIoT) applications in which parts of business-oriented enterprise systems, decoupled via fine-grained microservices, can be deployed in Fog networks in proximity to sensor devices. It boosts the responsiveness to IIoT events significantly where timely decision-making and actions are required, particularly for problematic trends and anomalies. However, microservice placement is a non-trivial problem in IIoT settings, given the dynamic occurrence of different IIoT events and variable resource needs of microservices against resource-constrained, heterogeneous, and distributed Fog devices. The cost of matching and deploying microservices in real-time, highly dynamic, and contentious settings can easily exceed the benefit of just-in-time Edge deployments. Current studies largely focus on managing the microservice placement from Cloud servers and offloading to the Cloud when Fog devices are resource-deficient. Yet, there is a lack of research focusing on efficient Fog resource use and managing the placement at the edge to improve the responsiveness. The complexity increases when multiple inter-dependent microservices with similar priorities are required to be placed on the same Fog devices. To mitigate this, we develop a tiered framework for dynamic microservice placement, in which costly resource decision-making for microservices is dedicated to Master Fog devices and Citizen Fog devices are exclusively assigned to executing microservice requests. Firstly, we implement a priority-based algorithm in each Master Fog device to identify the high-priority Edge-required microservices. Secondly, we sort the Fog devices according to their resource availability and place the microservices based on their priority and dependencies. Thirdly, we implement the strategies for microservice placement, scaling and request escalation in each Master Fog, managing a small number of Citizen Fog devices. These exempt the Citizen Fog devices from common resource and communication management responsibilities and enable them to allocate more resources to Edge-required microservices with a higher number of instances. Finally, we implement and evaluate our framework in a simulated Fog environment. The results outperform state-of-the-art frameworks in terms of efficient resource utilisation, resulting in reduced Cloud dependency by one-third compared to other approaches and average application execution time by 65–70%.

Adaptively identify and refine ill-posed regions for accurate stereo matching

Stereo matching cost constrains the consistency between pixel pairs. However, the consistency constraint becomes unreliable in ill-posed regions such as occluded or ambiguous regions of the images, making it difficult to explore hidden correspondences. To address this challenge, we introduce an Error-area Feature Refinement Mechanism (EFR) that supplies context features for ill-posed regions. In EFR, we innovatively obtain the suspected error region according to aggregation perturbations, then a simple Transformer module is designed to synthesize global context and correspondence relation with the identified error mask. To better overcome existing texture overfitting, we put forward a Dual-constraint Cost Volume (DCV) that integrates supplementary constraints. This effectively improves the robustness and diversity of disparity clues, resulting in enhanced details and structural accuracy. Finally, we propose a highly accurate stereo matching network called Error-rectify Feature Guided Stereo Matching Network (ERCNet), which is based on DCV and EFR. We evaluate our model on several benchmark datasets, achieving state-of-the-art performance and demonstrating excellent generalization across datasets. The code is available at https://github.com/dean7liu/ERCNet_2023.

Quick or cheap? Breaking points in dynamic markets

We examine two-sided markets where players arrive stochastically over time. The cost of matching a client and provider is heterogeneous, and the distribution of costs – but not their realization – is known. In this way, a social planner is faced with two contending objectives:(a) to reduce the players’ waiting time before getting matched; and (b) to reduce matching costs. In this paper, we aim to understand when and how these objectives are incompatible. We identify two regimes dependent on the ‘speed of improvement’ of the cost of matching with respect to market size. One regime results in a quick or cheap dilemma without ‘free lunch’: there exists no clearing schedule that is simultaneously optimal along both objectives. In that regime, we identify a unique breaking point signifying a stark reduction in matching cost contrasted by an increase in waiting time. The other regime features a window of opportunity in which free lunch can be achieved. Which scheduling policy is optimal depends on the heterogeneity of match costs. Under limited heterogeneity, e.g., when there is a finite number of possible match costs, greedy scheduling is approximately optimal, in line with the related literature. However, with more heterogeneity greedy scheduling is never optimal. Finally, we analyze a particular model where match costs are exponentially distributed and show that it is at the boundary of the no-free-lunch regime We then characterize the optimal clearing schedule for varying social planner desiderata.

Uncertainty propagation in stereo matching using copulas

This contribution presents a concrete example of uncertainty propagation in a stereo matching pipeline. It considers the problem of matching pixels between pairs of images whose radiometry is uncertain and modeled by possibility distributions. Copulas serve as dependency models between variables and are used to propagate the imprecise models. The propagation steps are detailed in the simple case of the Sum of Absolute Difference cost function for didactic purposes. The method results in an imprecise matching cost curve. To reduce computation time, a sufficient condition for conserving possibility distributions after the propagation is also presented. Finally, results are compared with Monte Carlo simulations, indicating that the method produces envelopes capable of correctly estimating the matching cost.

Improved YOLOv5 Based on Multi-Strategy Integration for Multi-Category Wind Turbine Surface Defect Detection

Wind energy is a renewable resource with abundant reserves, and its sustainable development and utilization are crucial. The components of wind turbines, particularly the blades and various surfaces, require meticulous defect detection and maintenance due to their significance. The operational status of wind turbine generators directly impacts the efficiency and safe operation of wind farms. Traditional surface defect detection methods for wind turbines often involve manual operations, which suffer from issues such as high subjectivity, elevated risks, low accuracy, and inefficiency. The emergence of computer vision technologies based on deep learning has provided a novel approach to surface defect detection in wind turbines. However, existing datasets designed for wind turbine surface defects exhibit overall category scarcity and an imbalance in samples between categories. The algorithms designed face challenges, with low detection rates for small samples. Hence, this study first constructs a benchmark dataset for wind turbine surface defects comprising seven categories that encompass all common surface defects. Simultaneously, a wind turbine surface defect detection algorithm based on improved YOLOv5 is designed. Initially, a multi-scale copy-paste data augmentation method is proposed, introducing scale factors to randomly resize the bounding boxes before copy-pasting. This alleviates sample imbalances and significantly enhances the algorithm’s detection capabilities for targets of different sizes. Subsequently, a dynamic label assignment strategy based on the Hungarian algorithm is introduced that calculates the matching costs by weighing different losses, enhancing the network’s ability to learn positive and negative samples. To address overfitting and misrecognition resulting from strong data augmentation, a two-stage progressive training method is proposed, aiding the model’s natural convergence and improving generalization performance. Furthermore, a multi-scenario negative-sample-guided learning method is introduced that involves incorporating unlabeled background images from various scenarios into training, guiding the model to learn negative samples and reducing misrecognition. Finally, slicing-aided hyper inference is introduced, facilitating large-scale inference for wind turbine surface defects in actual industrial scenarios. The improved algorithm demonstrates a 3.1% increase in the mean average precision (mAP) on the custom dataset, achieving 95.7% accuracy in mAP_50 (the IoU threshold is half of the mAP). Notably, the mAPs for small, medium, and large targets increase by 18.6%, 16.4%, and 6.8%, respectively. The experimental results indicate that the enhanced algorithm exhibits high detection accuracy, providing a new and more efficient solution for the field of wind turbine surface defect detection.

SD-MVS: Segmentation-Driven Deformation Multi-View Stereo with Spherical Refinement and EM Optimization

In this paper, we introduce Segmentation-Driven Deformation Multi-View Stereo (SD-MVS), a method that can effectively tackle challenges in 3D reconstruction of textureless areas. We are the first to adopt the Segment Anything Model (SAM) to distinguish semantic instances in scenes and further leverage these constraints for pixelwise patch deformation on both matching cost and propagation. Concurrently, we propose a unique refinement strategy that combines spherical coordinates and gradient descent on normals and pixelwise search interval on depths, significantly improving the completeness of reconstructed 3D model. Furthermore, we adopt the Expectation-Maximization (EM) algorithm to alternately optimize the aggregate matching cost and hyperparameters, effectively mitigating the problem of parameters being excessively dependent on empirical tuning. Evaluations on the ETH3D high-resolution multi-view stereo benchmark and the Tanks and Temples dataset demonstrate that our method can achieve state-of-the-art results with less time consumption.

MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application

Multi-view stereo plays an important role in 3D reconstruction but suffers from low reconstruction efficiency and has difficulties reconstructing areas with low or repeated textures. To address this, we propose MVP-Stereo, a novel multi-view parallel patchmatch stereo method. MVP-Stereo employs two key techniques. First, MVP-Stereo utilizes multi-view dilated ZNCC to handle low texture and repeated texture by dynamically adjusting the matching window size based on image variance and using a portion of pixels to calculate matching costs without increasing computational complexity. Second, MVP-Stereo leverages multi-scale parallel patchmatch to reconstruct the depth map for each image in a highly efficient manner, which is implemented by CUDA with random initialization, multi-scale parallel spatial propagation, random refinement, and the coarse-to-fine strategy. Experiments on the Strecha dataset, the ETH3D benchmark, and the UAV dataset demonstrate that MVP-Stereo can achieve competitive reconstruction quality compared to state-of-the-art methods with the highest reconstruction efficiency. For example, MVP-Stereo outperforms COLMAP in reconstruction quality by around 30% of reconstruction time, and achieves around 90% of the quality of ACMMP and SD-MVS in only around 20% of the time. In summary, MVP-Stereo can efficiently reconstruct high-quality point clouds and meet the requirements of several photogrammetric applications, such as emergency relief, infrastructure inspection, and environmental monitoring.

Improving the accuracy of wind speed spatial interpolation: A pre-processing algorithm for wind speed dynamic time warping interpolation

Wind power is one of the most vital renewable energy resources in the world. Wind energy production is directly correlated with the quality and quantity of wind speed data. Interpolation techniques can be employed to fill in the gaps in the current wind speed observation data series. However, existing methods for obtaining blank data do not pre-regulate the regional spatial wind speed sequence and instead rely on direct interpolation, which leads to low accuracy in the interpolation. This is not a problem with the model itself, but rather with the wind speed flowing in space and exhibiting sequence misalignment on the timeline. To address this issue, this study proposes a Wind Speed Dynamic Time Warping (WSDTW) algorithm based on Dynamic Time Warping (DTW) to match similar wind speed reduction sequences in terms of time error. We used the shape context descriptor to encode wind speed and introduced wind rose descriptors to represent wind direction initially. The matching cost of DTW was then optimized. Finally, five common interpolation methods were selected to evaluate the method. The research results indicate that interpolation after WSDTW matching and warping can significantly improve the accuracy of wind speed interpolation and reduce the spatial dependence of wind speed. This method demonstrates good stability and generalization, performing exceptionally well in situations where there are gaps in regional wind speed data or missing data.

Dual-stream pyramid and attention network for stereo matching

In order to accurately estimate the disparity of ill-posed regions, such as weak texture and occlusion regions, we propose DSPANet, a stereo matching network that incorporates a dual-stream pyramid module and a channel and spatial attention module. The dual-stream pyramid module captures numerous complementary features from different layers by utilizing multi-resolution inputs and feature extraction blocks. This approach enables the learning of local detailed features at various scales. These features at various scales are then combined to calculate the stereo matching cost. By incorporating channel and spatial attention module into the feature extraction process to obtain richer and more concise contextual information, the matching cost can be constructed more accurately, which provides powerful conditions for subsequent cost aggregation. In the cost aggregation stage, we utilize the stacked hourglass module for both encoding and decoding. Additionally, we incorporate 3D global attention upsampling during the decoding stage, which enables high-level features to provide guidance information to low-level features in a simple way. We evaluate our proposed method on the Scene Flow dataset, as well as the KITTI2012 and KITTI2015 datasets. The experimental results demonstrate that our DSPANet achieves superior performance and effectively enhances the matching results in ill-posed regions. Our code has been implemented using PyTorch and will be released after paper publication at https://github.com/Shi-LiQing/DSPANet.

Matching cost function analysis and disparity optimization for low-quality binocular images

State-of-the-art dense stereo matching algorithms have achieved excellent performance, demonstrating a capability to attain precise matching in most areas. However, current such methods rarely achieve this when images are captured under poor conditions. To improve the accuracy of the algorithm in such cases, this paper introduces a post-optimization algorithm to rectify matching errors and enhance outcomes. The main research areas of this paper include three aspects. (1) Disparities are classified into reliable and unreliable results based on the analysis of geometric matching relationships, local features in the images, and components within the matching cost function; (2) Subsequent analysis of horizontal image features identifies local characteristic indices calculated through integration along the horizontal axis, which establish specific matching criteria, forming the foundation for a cost volume that encompasses these distinct matches; (3) A redefined matching cost function is applied to previously classified unreliable results to rectify matching errors. This energy function is based on the cost volume above. Experimental results validate the efficacy of the proposed post-optimization algorithm, reducing the average matching errors from 8.66% to 5.85%.

OrangeStereo: A navel orange stereo matching network for 3D surface reconstruction

Three-dimensional (3D) surface reconstruction is crucial to accurately detecting navel orange external qualities such as size and shape, and binocular stereo vision is promising for practical sorting scenarios due to its high frame rates and ease of deployment. For objects with texture-less surface, such as navel oranges, it is very challenging to match the corresponding points using traditional stereo matching algorithms because the detected feature points have high similarity in color as well as structure. Most of the existing deep learning-based stereo matching algorithms focus on the stereo matching performance of the whole image and lack the attention to the matching performance of the main region. Meanwhile, simple background with less texture will bring more uncertainty to the matching of corresponding points, which affects the robustness of the model. The OrangeStereo was proposed based on the GwcNet structure to achieve a better corresponding points matching effect of navel oranges, which can provide more accurate 3D surface information. The Structural Features Extraction (SFE) module was introduced after the initial feature extraction module to extract stable structural features and avoid the limitations of using single convolutional features. The Attention Weights Generation (AWG) module was utilized at matching cost calculation stage to help suppress redundant information and make the network pay more attention to the matching performance of specific regions. The improved loss function assigned a greater weight to the loss of the navel orange region using semantic information from left image to make the network more focused on the matching effect of the navel orange region. An ablation study was conducted to confirm the efficiency of the SFE module, AWG module, and the improved loss function of the algorithm. The ablation study contained five sub-experiments, including the baseline model, adding the SFE and the AWG modules respectively, and whether to add the improved loss function based on the addition of previous two modules. Additionally, PSMNet, GwcNet, RealtimeStereo, and ACVNet, four typical stereo matching methods, were contrasted with the OrangeStereo. The proposed OrangeStereo achieved better performance with EPE, RMSE, bad-1, bad-3, bad-5, and inference time of 0.60 pixels, 3.53 pixels, 2.37 %, 1.83 %, 1.62 %, and 33 ms in the orange region, respectively. The R2 and RMSE for the depth of orange utilizing our method were 0.982 and 0.81 mm, respectively. The proposed algorithm can be employed to obtain accurate depth information of fruits quickly, and it is expected to be used in the 3D reconstruction of fruits in the actual commercial fruit sorting lines to achieve more accurate external quality assessment.

Stereoscopic Image Enabled Animation Art Design Exploration

Abstract With the emergence of movie art, many related art forms were born. Animation art has gained a large number of audiences due to its exaggerated expression. In order to improve the problems of character image overlapping and scene distortion that may occur in the original two-dimensional space, this paper constructs a semi-global stereo image matching algorithm based on the use of point cloud matching in the animation screen for the determination of the screen position and the morphology of the animated character. To compensate for the radiation difference in the stereo image matching algorithm, object-square geometric constraints are proposed to improve the matching cost calculation process. After calculating the matching cost, the original animation model is optimized or rebuilt by creating a depth space image. Taking Disney animation characters as an example, the number of joints required for the reconstruction of the “Snow White” character is 2.9 times more than that of the “Mickey” character, but the reconstruction time of the skeleton is only 12 times longer than that of the “Mickey” character. However, the skeleton reconstruction time is only 12.1 seconds longer than that of the “Mickey” character, which indicates that the algorithm proposed in this paper does not produce a large difference in reconstruction time due to the difference in characters. For the construction of animated indoor scenes, in the eight experimental scenes, the maximum number of grids can be divided into 42365412, and the processing time required is 276.413 seconds. It shows that the algorithm in this paper is capable of subregionally refining the indoor space of the animation. The proposed semi-global stereo image matching-based pan can quickly reconstruct animated character models and animated scenes, as can be seen.

A new stereo matching algorithm based on improved four-moded census transform and adaptive cross pyramid model

<p>Stereo matching is still very challenging in terms of depth discontinuity, occlusions, weak texture regions, and noise resistance. To address the problems of poor noise immunity of local stereo matching and low matching accuracy in weak texture regions, a stereo matching algorithm (iFCTACP) based on improved four-moded census transform (iFCT) and a novel adaptive cross pyramid (ACP) structure were proposed. The algorithm combines the improved four-moded census transform matching cost with traditional measurement methods, which allows better anti-interference performance. The cost aggregation is performed on the adaptive cross pyramid structure, a unique structure that improves the traditional single mode of the cross. This structure not only enables regions with similar color and depth to be connected but also achieves cost smoothing across regions, significantly reducing the possibility of mismatch due to inadequate corresponding matching information and providing stronger robustness to weak texture regions. Experimental results show that the iFCTACP algorithm can effectively suppress noise interference, especially in illumination and exposure. Furthermore, it can markedly improve the error matching rate in weak texture regions with better generalization. Compared with some typical algorithms, the iFCTACP algorithm exhibits better performance whose average mismatching rate is only 3.33$ \% $.</p>

Geomagnetic indoor localisation-based dilated convolution neural networks

ABSTRACT With the rapid development of mobile Internet, the demand for Indoor Location-Based Service (ILBS) keeps rising. Geomagnetic field fingerprinting-based positioning scheme, with low cost, high accuracy and good stability, has gradually earned the attention of researchers. There are many problems in the existing indoor localisation researches based on geomagnetic fingerprint recognition, such as heavy workload of fingerprint collection, insufficient positioning accuracy, high walking cost of positioning and long positioning delay. Therefore, a localisation system named DCGIL is proposed in this thesis. In DCGIL, a segmentation length estimation algorithm of geomagnetic fingerprint sequence is proposed to evaluate the minimum segmentation length, the shortest walking distance. Then, through an overlapping fingerprint segmentation method, the gap between coordinate points and the cost of fingerprint matching are reduced. Further, dilated convolutional neural networks is utilised for segmented fingerprint classification, which could improve the accuracy of fingerprint classification compared with the existing classification methods. Finally, DCGIL utilises the sliding window to align and match the segmented fingerprint. Experiments show that the localisation accuracy reached 1.21 metres at 3.8 m shortest walking distance derived from segmentation length estimation algorithm, which improved 28% compared to existing algorithms.