Object Features Research Articles

Enhanced Abandoned Object Detection through Adaptive Dual-Background Modeling and SAO-YOLO Integration

Abandoned object detection is a critical task in the field of public safety. However, existing methods perform poorly when detecting small and occluded objects, leading to high false detection and missed detection rates. To address this issue, this paper proposes an abandoned object detection method that integrates an adaptive dual-background model with SAO-YOLO (Small Abandoned Object YOLO). The goal is to reduce false and missed detection rates for small and occluded objects, thereby improving overall detection accuracy. First, the paper introduces an adaptive dual-background model that adjusts according to scene changes, reducing noise interference in the background model. When combined with an improved PFSM (Pixel-based Finite State Machine) model, this enhances detection accuracy and robustness. Next, a network model called SAO-YOLO is designed. Key improvements within this model include the SAO-FPN (Small Abandoned Object FPN) feature extraction network, which fully extracts features of small objects, and a lightweight decoupled head, SODHead (Small Object Detection Head), which precisely extracts local features and enhances detection accuracy through multi-scale feature fusion. Finally, experimental results show that SAO-YOLO increases mAP@0.5 and mAP@0.5:0.95 by 9.0% and 5.1%, respectively, over the baseline model. It outperforms other advanced detection models. Ultimately, after a series of experiments on the ABODA, PETS2006, and AVSS2007 datasets, the proposed method achieved an average detection precious of 91.1%, surpassing other advanced methods. It significantly outperforms other advanced detection methods. This approach notably reduces false and missed detections, especially for small and occluded objects.

Object Detection Algorithm for Citrus Fruits Based on Improved YOLOv5 Model

To address the challenges of missed and false detections in citrus fruit detection caused by environmental factors such as leaf occlusion, fruit overlap, and variations in natural light in hilly and mountainous orchards, this paper proposes a citrus detection model based on an improved YOLOv5 algorithm. By introducing receptive field convolutions with full 3D weights (RFCF), the model overcomes the issue of parameter sharing in convolution operations, enhancing detection accuracy. A focused linear attention (FLA) module is incorporated to improve the expressive power of the self-attention mechanism while maintaining computational efficiency. Additionally, anchor boxes were re-clustered based on the shape characteristics of target objects, and the boundary box loss function was improved to Foal-EIoU, boosting the model’s localization ability. Experiments conducted on a citrus fruit dataset labeled using LabelImg, collected from hilly and mountainous areas, showed a detection precision of 95.83% and a mean average precision (mAP) of 79.68%. This research not only significantly improves detection performance in complex environments but also provides crucial data support for precision tasks such as orchard localization and intelligent picking, demonstrating strong potential for practical applications in smart agriculture.

Self-supervised monocular depth estimation based on improved dense network and wavelet decomposition

Traditional self-supervised monocular depth estimation models do not adequately extract and fuse shallow features, which can easily lead to problems such as missed detection of small objects and blurred object edges. To address the above problems, this paper proposes a self-supervised monocular depth estimation model based on improved dense network and wavelet decomposition. The model follows the structure of U-net, in which the encoder adopts an improved dense connection method to improve the feature extraction and fusion capabilities of the encoder; at the same time, a detail enhancement module is added to the jump connection to further refine and integrate the multi-scale features output by the encoder; finally, the wavelet decomposition method is introduced in the decoder, forcing the decoder to pay more attention to high-frequency information and achieve refined processing of image edges. Experimental results show that the depth estimation model proposed in this paper has a stronger ability to capture small object features, and the edges of the generated depth map are clearer and more accurate.

A Lightweight Strip Steel Surface Defect Detection Network Based on Improved YOLOv8

Strip steel surface defect detection has become a crucial step in ensuring the quality of strip steel production. To address the issues of low detection accuracy and long detection times in strip steel surface defect detection algorithms caused by varying defect sizes and blurred images during acquisition, this paper proposes a lightweight strip steel surface defect detection network, YOLO-SDS, based on an improved YOLOv8. Firstly, StarNet is utilized to replace the backbone network of YOLOv8, achieving lightweight optimization while maintaining accuracy. Secondly, a lightweight module DWR is introduced into the neck and combined with the C2f feature extraction module to enhance the model’s multi-scale feature extraction capability. Finally, an occlusion-aware attention mechanism SEAM is incorporated into the detection head, enabling the model to better capture and process features of occluded objects, thus improving performance in complex scenarios. Experimental results on the open-source NEU-DET dataset show that the improved model reduces parameters by 34.4% compared with the original YOLOv8 algorithm while increasing average detection accuracy by 1.5%. And it shows good generalization performance on the deepPCB dataset. Compared with other defect detection models, YOLO-SDS offers significant advantages in terms of parameter count and detection speed. Additionally, ablation experiments validate the effectiveness of each module.

Objective and subjective accounts of urban exposures for epidemiological research on mental health. Measurement and analysis

The interest in the impact of urban environmental exposures (UrbEEs) on mental health has greatly increased in the last two decades. Researchers have tended to measure said exposures either via...

Group-Based Distinctive Image Captioning with Memory Difference Encoding and Attention

AbstractRecent advances in image captioning have focused on enhancing accuracy by substantially increasing the dataset and model size. While conventional captioning models exhibit high performance on established metrics such as BLEU, CIDEr, and SPICE, the capability of captions to distinguish the target image from other similar images is under-explored. To generate distinctive captions, a few pioneers employed contrastive learning or re-weighted the ground-truth captions. However, these approaches often overlook the relationships among objects in a similar image group (e.g., items or properties within the same album or fine-grained events). In this paper, we introduce a novel approach to enhance the distinctiveness of image captions, namely Group-based Differential Distinctive Captioning Method, which visually compares each image with other images in one similar group and highlights the uniqueness of each image. In particular, we introduce a Group-based Differential Memory Attention (GDMA) module, designed to identify and emphasize object features in an image that are uniquely distinguishable within its image group, i.e., those exhibiting low similarity with objects in other images. This mechanism ensures that such unique object features are prioritized during caption generation for the image, thereby enhancing the distinctiveness of the resulting captions. To further refine this process, we select distinctive words from the ground-truth captions to guide both the language decoder and the GDMA module. Additionally, we propose a new evaluation metric, the Distinctive Word Rate (DisWordRate), to quantitatively assess caption distinctiveness. Quantitative results indicate that the proposed method significantly improves the distinctiveness of several baseline models, and achieves state-of-the-art performance on distinctiveness while not excessively sacrificing accuracy. Moreover, the results of our user study are consistent with the quantitative evaluation and demonstrate the rationality of the new metric DisWordRate.

Organizational and legal principles of forensic examination in the field of road infrastructure

The aim of the work is to analyze the offenses committed in the field of road infrastructure and to identify the main areas of criminal activity, for which investigation specific expertise in the form of forensic examination is applied. The methodological basis of the study of this problem was general, separate and special cognition methods. The results of performed research demonstrate possibility of distinguishing two main areas of criminal activity in the field of road infrastructure. These are official crimes related to the field of official and professional activity, as well as war crimes related to unforeseen destruction of road infrastructure. Thus, one of socially dangerous manifestations in this area are crimes related to road construction. While investigating issues of compliance of the developed project-technical and estimate documentation with the requirements of regulatory acts in the field of road construction, as well as questions of determining compliance of the completed construction works and built objects with the project-technical documentation, often arise. Such issues can be resolved if forensic expert (experts) have relevant professional knowledge in the design, construction and operation of roads, taking into account the characteristics of road infrastructure objects. At the current stage, when all Ukrainians feel consequences of Russian aggression, another problem is clearly visible. After all, for all the crimes of this shameful war, the enemy must suffer a just punishment, and the victims must receive compensation for the material and moral damage caused to them. In connection with the emergence of new tasks that arise during the investigation of war crimes committed by the aggressor country, the question of the appropriate level of expert provision of justice in conditions of war that is directly related both to increasing effectiveness of the inspection of the scene of the event and to further conducting forensic examinations. Taking into account the fact that a huge number of objects were damaged and destroyed, there was a need to use the latest methods and approaches to effectively counteract the specified manifestations of crime, in particular, development of effective system of expert justice provision at the stages of pre-trial investigation and subsequent judicial review of cases. Thereby it is important to provide expert investigation of crimes related to the establishment of damages to the state, individuals and legal entities from the destruction of immovable and movable property.

SOD‐YOLO: Small Object Detection Network for UAV Aerial Images

With the rapid development of the UAV industry, object detection using UAV has become a research hotspot. However, most current object detection models based on deep learning have large parameter counts and are difficult to deploy on embedded devices with limited memory and computational power. To address this problem, a Small Object Detection network for UAV aerial images SOD‐YOLO based on YOLOv8 is proposed, which can meet the application requirements of resource‐constrained devices while ensuring the detection accuracy. First, cross‐domain fusion attention (CDFA) mechanism is proposed to build the C2f‐Attention module in this paper, which is embedded in the backbone network in order to improve the extraction capability of key object features. Meanwhile, the AIFI_LSPE feature fusion module with improved RT‐DETR and the IoU‐aware query selection mechanism are added to the path aggregation network to improve the accuracy of multi‐scale object detection. In addition, in order to balance the sample size ratio and improve the robustness of the network model, we make a new UAV image dataset named VisDrone2019 Extended Edition (VDEE) using images from the VisDrone2019 and UAVDT public datasets. Finally, Shape‐IoU is used as a loss function to reduce the difference between the object GT frame and the detection frame. Experiments show that SOD‐YOLO has a mAP@0.5 of 42.8% in the VDEE dataset, which is increased by 5.1% over YOLOv8. In the VisDrone2019 dataset mAP@0.5 is 39.2%, an improvement of 5.8% over YOLOv8. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Multi-scale sensing and multi-dimensional feature enhancement for surface defect detection of hot-rolled steel strip

ABSTRACT Surface defect detection of hot-rolled steel strip is a crucial process in its production. Aiming at the problem of poor detection accuracy caused by complex shape of defect features and imbalance between object scale and feature scale, this paper proposes a multi-scale sensing and multi-dimensional feature enhancement algorithm. Firstly, a multi-branch residual aggregation module (MRAM) is proposed, which uses the interactive features of different receptive fields between layers to capture global and local texture features, improving the feature extraction capability for defects of different scales. Then, the multi-dimensional attention enhancement head (MAEH) is proposed, which integrates feature information from different prediction branches and realises feature information enhancement in scale, space and channel dimensions for the aggregated information to improve the accuracy and robustness of steel surface defect detection. The results show that the proposed algorithm achieves 82.7% mAP and 89 FPS on the NEU-DET dataset, and 73.3% mAP and 94 FPS on the GC10-DET dataset. Compared with state-of-the-art algorithms, the proposed algorithm has the highest mAP, strong generalisation ability, and good real-time performance, which proves its effectiveness in detecting hot-rolled steel strip surface defects.

Determination of outliers when building a multivariate regression model of prices in residential real estate market of the region

The paper is aimed at selecting the optimal method for identifying outliers in the initial data when building a multivariate regression model of prices in the regional residential real estate market. The study was based on offers for sale of apartments in prefab flat blocks located in Irkutsk. In this regard, a basic multiplicative multivariate regression model was built, describing the dependence of cost indicators on the pricing factors of real estate. The identified outliers were iteratively removed from the basic model. The methods for detecting outliers included calculation of standard deviation (z-score), calculation of the Mahalanobis distance, as well as a method developed in the study for bringing the prices of objects to the characteristics of the reference object. The optimal method for detecting outliers in the initial data was selected by comparing the characteristics of the final variable-based multivariate regression models obtained after removing outliers from them. The analysis of the results proved the method of bringing the prices of objects to the characteristics of the reference object to be the optimal method of identifying outliers when building a multivariate regression model of prices in the regional residential real estate market. This method significantly reduces the approximation errors of the basic multivariate regression model of the market, thereby increasing the adequacy of the results of the real estate valuation conducted on its basis.

THE INFLUENCE OF A GENERATIVE LEARNING MODEL BASED ON WAVE MATERIAL COGNITIVE CONFLICT ON STUDENT LEARNING OUTCOMES AT SMAN 5 PAYAKUMBUH

Student learning outcomes in Physics subjects, especially in Wave material, are still low. This is caused by the learning model used in the Wave material not being being the process standards and characteristics of wave learning objectives. A generative learning model based on cognitive conflict is one approach to solving the aforementioned issue. The purpose of this study is to ascertain how student learning outcomes at SMAN 5 Payakumbuh are affected by the use of a generative learning model based on cognitive conflict in Wave content. The type of research is quasi-experimental research with a pretest-posttest Control Group Design. The participants in this study were all SMAN 5 Payakumbuh science class XI students. The information gathered reflects the attitudes, knowledge, and abilities of the pupils toward learning. Two averages are tested for equality as a data analysis technique. The study's findings demonstrate the notable distinctions between classrooms that employ the Problem-Based Learning model and those that employ the Generative learning model based on cognitive conflict. The average student learning outcome in classes that apply the Generative learning model is 83.14 and in classes that apply the Problem-Based Learning model is 77.26. The influence of the generative learning model based on cognitive conflict can be seen in the learning outcomes of students who have been analyzed and hypothesis tested. Based on the hypothesis test, the ttable is 2.006 and the tcount is 2.1. The condition for H0 to be rejected is if ttable < tcount. The tcount value is within the rejection of H0, so HI is accepted. Because all variables are controlled, except the learning model, it can be said that the application of the Generative learning model based on cognitive conflict in Wave material has a positive effect on student learning outcomes.

Beyond pain privacy and pain meters: a new vision for pain biomarkers.

To an individual, pain is unambiguously real. To a caregiver, assessing pain in others is a challenging process shrouded in doubt. To explain this challenge, many assume that pain "belongs" exclusively to the bearer of that experience and accept the dogma that pain is private. However, privacy also entails that it is not possible to identify, share, or communicate that experience with others. Obviously, this is not true and the consequences of pain privacy would be devastating for healthcare. Pain is indeed unique and subjective, but not necessarily private. Pain is in fact readily communicable, though perhaps not as effectively and reliably as caregivers would like. On the other hand, healthcare systems mandate objective metrics in pain diagnosis. Smiley face caricatures are a staple of clinical practice and a universal standard for reporting pain levels. These conditions create a double paradox: Assess a private experience that is inaccessible, and use numerical scales to measure subjective attributes. Navigating this stressful environment, medical professionals experience intellectual dissonance, patients are frustrated, and value-based care is undermined. Offering a way out, first, we refute the privacy and objectification of pain citing philosophical, behavioral, and neuroscientific arguments. We discuss Wittgensteinian views against privacy, explore the clear evolutionary advantage of communicating pain to others, and identify neural circuits in the mammalian brain that contribute to empathy. Second, we highlight the subjectivity of pain, embracing the complexity and uniqueness of an individual's pain. We also provide compelling evidence for brain mechanisms that actively shape the pain experience according to predictive coding principles. Third, we offer a vision for the development of biomarker technologies that assess pain fairly without engendering bias against the patient's narrative. Our recommendations are based on the overwhelming appreciation that "medicine by emoji" is inadequate for capturing the multidimensional nature of pain. Our view is that the most promising candidates for pain biomarkers consist of self-reports as ground truth augmented by physiological signatures of biological relevance to pain. Integration of subjective and objective multimodal features will be key for the development of comprehensive pain assessment models.

Text-Guided Multi-Class Multi-Object Tracking for Fine-Grained Maritime Rescue

The rapid development of remote sensing technology has provided new sources of data for marine rescue and has made it possible to find and track survivors. Due to the requirement of tracking multiple survivors at the same time, multi-object tracking (MOT) has become the key subtask of marine rescue. However, there exists a significant gap between fine-grained objects in realistic marine rescue remote sensing data and the fine-grained object tracking capability of existing MOT technologies, which mainly focuses on coarse-grained object scenarios and fails to track fine-grained instances. Such a gap limits the practical application of MOT in realistic marine rescue remote sensing data, especially when rescue forces are limited. Given the promising fine-grained classification performance of recent text-guided methods, we delve into leveraging labels and attributes to narrow the gap between MOT and fine-grained maritime rescue. We propose a text-guided multi-class multi-object tracking (TG-MCMOT) method. To handle the problem raised by fine-grained classes, we design a multi-modal encoder by aligning external textual information with visual inputs. We use decoding information at different levels, simultaneously predicting the category, location, and identity embedding features of objects. Meanwhile, to improve the performance of small object detection, we also develop a data augmentation pipeline to generate pseudo-near-infrared images based on RGB images. Extensive experiments demonstrate that our TG-MCMOT not only performs well on typical metrics in the maritime rescue task (SeaDronesSee dataset), but it also effectively tracks open-set categories on the BURST dataset. Specifically, on the SeaDronesSee dataset, the Higher Order Tracking Accuracy (HOTA) reached a score of 58.8, and on the BURST test dataset, the HOTA score for the unknown class improved by 16.07 points.

One point is all you need for weakly supervised object detection

Object detection with weak annotations has attracted much attention recently. Weakly supervised object detection(WSOD) methods which only use image-level labels to train a detector encounter some severe problems that it cannot cover the whole object and the region proposal methods waste a large amount of time. Meanwhile, point supervised object detection(PSOD) leverages point annotations that remarkably improves the performance. However, point annotation is still complex and increase the costs of annotation. To overcome these issues, we propose a novel method which only requires one point per category for a training image. Compared to point annotation, our method significantly reduces the annotation cost as the number of point annotations is largely reduced. We design a framework to train a detector with one point per category annotation. Firstly, a pseudo box generation module is introduced to generate the corresponding pseudo boxes of the annotated points. Then, inspired by the observation that the features of objects with the same class in an image are very similar, a dense instances mining module is proposed to make use of the similarity between the features of objects with the same class to discover unlabeled instances and generate pseudo category heatmaps. Finally, the pseudo boxes and pseudo category heatmaps are leveraged to train a detector. Experiments conducted on popular open-source datasets verify the effectiveness of our annotation method and framework. Our proposed method outperforms previous WSOD methods and achieves comparable performance with some PSOD methods in a more efficient way.

Автоматизований метод перевірки та налагодження тестових сценаріїв на основі формальної моделі

Introduction. Model-based test cases generation is a popular strategy for test automation. It helps to reduce time spent on the development of a test suite and can improve level of coverage. However, many reports show shortage of such test cases in poor quality and doubtable efficiency. Purpose. The main goal of the proposed method is cost-effective validation, assessment, debugging and concretization of generated test cases. The method helps improve quality and efficiency of the test cases, make their scenario meaningful and goal-oriented. The method also develops debugging facilities and simplifies data dependency analysis and test scenario editing. Methods. We propose an automated post-processing method which allows to evaluate path that is examined by the test case, and to make safe changes to the path which will eliminate the shortcomings while leaving the coverage targets of the test case unharmed. The method is based on visualization of the path along the control flow graph of the model with additional information about factual evaluation history of all variables and possible alternative variants of behavior. For consistent substitution of certain values in the signal parameters, which would determine the artifacts of the test environment (such as, for example, files, databases, etc.) and check boundary cases (in predicates of conditions, indexing of arrays, etc.), a method of interactive specification of symbolic traces has been developed. Results. The role of the user in deciding whether to add a test case to the project test suite and make changes to it remains crucial, but to reduce labor intensity, the following processes are automated: evaluation of test scenarios according to certain objective characteristics (level of coverage, ability to detect defects, data cohesion, etc.); highlighting of possible alternatives for making corrections; consistent updating of computations for the corresponding corrections. A prototype was developed based on the proposed methods. The empirical results demonstrated a positive impact on the overall efficiency (ability to detect defects and reduce resource consumption) and quality (meaningfulness, readability, maintenance, usefulness for debugging, etc) of the generated test suites. The method allows to make automatically generated test cases trustable and usable. Conclusion. The proposed toolkit significantly reduces the time spent on researching the results of test generation and validation of the obtained tests and their editing. Unlike existing simulation methods, the proposed method not only informs about the values of variables, but also explores the history of their computations and additionally provides information about admissible alternatives. Further we plan to improve the process of localizing the causes of test failure at execution phase to speed up the search for defects.

Features of High-Precision Photothermal Analysis of Liquid Systems by Dual-Beam Thermal Lens Spectrometry.

Thermal lens spectrometry is a high-sensitivity method for measuring the optical and thermal parameters of samples of different nature. To obtain both thermal diffusivity and absorbance-based signal measurements with high accuracy and precision, it is necessary to pay attention to the factors that influence the trueness of photothermal measurements. In this study, the features of liquid objects are studied, and the influence of optical and thermal effects accompanying photothermal phenomena are investigated. Thermal lens analysis of dispersed solutions and systems with photoinduced activity is associated with a large number of side effects, the impact of which on trueness is not always possible to determine. It is necessary to take into account the physicochemical properties and optical and morphological features of the nanophase and components exhibiting photoinduced activity. The results obtained make it possible to reduce systematic and random errors in determining the thermal-diffusivity-based and absorbance-based photothermal signals for liquid objects, and also contribute to a deeper understanding of the physicochemical processes in the sample.

Nearshore optical video object detector based on temporal branch and spatial feature enhancement

The computing power of nearshore and ship-borne devices is limited, posing significant challenges for accurately detecting objects in real-time on such devices. We propose a nearshore video object detector (NVID) to tackle these challenges. Considering the abundance of dynamic entities in the nearshore environment, we have developed you can look more (YCLM) to perceive the temporal characteristics of these objects. Furthermore, to improve the ability to detect objects of different sizes of networks, we designed parallel deformable attention (PDA) based on the spatial features of objects. More importantly, we developed fast re-parameterization convolution (FREConv) and faster conv (FConv). Building on these innovations, we proposed a fast re-parameterization network (FRENet) specifically tailored to produce low-parameter, multi-scale feature outputs. With end-to-end training, our pipeline outperforms other state-of-the-art (SOTA) methods on the nearshore objects (NearshoreObjects) dataset (90.4 average precision (AP) 50 (＋4.7), 9.3 parameters (Params) (−1.0M), 24.8 frames per second (FPS) (Jetson Nano) (＋0.6)). In addition, NVID also achieved excellent results in the on board (OnBoard) dataset (90.3 AP50 (＋2.8), 9.3 params (−1.0M), 26.5 FPS (Jetson Nano) (＋0.8)). The source code can be accessed at https://github.com/Yuanlin-Zhao/NVID.

Developing Concepts of Authenticity: Insights From Parents' and Children's Conversations About Historical Significance.

The present study investigated children's understanding that an object's history may increase its significance, an appreciation that underpins the concept of historical authenticity (i.e., the idea that an item's history determines its true identity, beyond its functional or material qualities, leading people to value real items over copies or fakes). We examined the development of historical significance through the lens of parent-child conversations, and children's performance on an authenticity assessment. The final sample was American, 79.2% monoracial White, and mid-high socio-economic status (SES) and included 48 parent-child pairs: 24 with younger children (R=3.5 to 4.5 years) and 24 with older children (R=5.5 to 6.5 years). Parent-child pairs discussed three books we created, with three storylines: a museum (culturally authentic) storyline, a clean-up (personally authentic) storyline, and a control storyline. Across measures, conversations suggested that authenticity may begin as a "placeholder concept" that is initially rooted in a broad appreciation for the significance of old objects and only later filled in with specifics. This placeholder initially directs children's learning about authenticity by linking, in an unspecified way, the value and significance of objects to their past. For example, we found that young children appropriately appealed to history (vs. perceptual or functional features of objects) in contexts regarding authentic objects but struggled in determining which objects were more significant on the post-test assessment, suggesting that they attend to object history but are not yet sure how histories matter for making authenticity judgments. We also found some evidence that directing children's attention toward conceptual information related to object history may in turn direct them away from material or perceptual considerations, as seen in trade-offs in parents' and children's conversations. Together, this exploratory report offers many new avenues for work on the development of authenticity concepts in childhood.

ETFE: Efficient transformer with feature enhancement for semantic segmentation

Abstract Despite the powerful capabilities of Transformers in relation to modeling, their effectiveness in capturing multi-scale object features in image-based tasks is often limited. This limitation arises from the diverse shapes and distances between objects in natural scenes, which pose challenges for Transformers to accurately capture object sizes and distances. To overcome this problem, we propose an effective Transformer which extracts multi-scale features at different resolutions. First, we leverage a multi-head attention mechanism to extract richer semantic features at various resolutions. The semantic features are then passed through the Feature Gate to aggregate multi-scale semantic features. Finally, Attentional Feature Enhancement further strengthens the semantic information of target features, enabling the modeling of dependencies among multi-scale features. Comprehensive experiments conducted on the Cityscapes and ADE20K datasets have shown that our model outperforms competing methods in both performance and accuracy.

Sports Player Action Recognition based on Deep Learning

A sports auxiliary evaluation system suitable for China’s national conditions was established using big data and sports identification technology. First of all, this paper extends the data of normative behavior and constructs a normative library of scores and comparisons. The acquisition of 3D data is emphasized. The method based on Fourier descriptors is used to locate the motion accurately. In this way, better gait recognition results can be obtained. The Fourier characteristics before and after wavelet transform are compared with the actual object characteristics, and the results show that the proposed algorithm can extract the features with high precision. This scheme can obtain a more accurate identification effect. The system proposed in this paper provides a powerful means for judges to score.