An algorithm for extracting similar segments of moving target trajectories based on shape matching

Ouyang, Zhihong; Xue, Lei; Ding, Feng; Li, Da

doi:10.1016/j.engappai.2023.107243

Cited by 3 publications

(2 citation statements)

References 53 publications

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“…Additionally, we compare ISLS with several classical traditional segmentation methods, including template matching [57], Canny edge segmentation [58], contour segmentation [59], PCA segmentation [60], and iForest segmentation [18]. The evaluation results of traditional leakage segmentation algorithms are shown in Table 4.…”

Section: Comparison With Other Segmentation Methodsmentioning

confidence: 99%

ISLS: An Illumination-Aware Sauce-Packet Leakage Segmentation Method

You,

Lin,

Feng

et al. 2024

Sensors

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The segmentation of abnormal regions is vital in smart manufacturing. The blurring sauce-packet leakage segmentation task (BSLST) is designed to distinguish the sauce packet and the leakage’s foreground and background at the pixel level. However, the existing segmentation system for detecting sauce-packet leakage on intelligent sensors encounters an issue of imaging blurring caused by uneven illumination. This issue adversely affects segmentation performance, thereby hindering the measurements of leakage area and impeding the automated sauce-packet production. To alleviate this issue, we propose the two-stage illumination-aware sauce-packet leakage segmentation (ISLS) method for intelligent sensors. The ISLS comprises two main stages: illumination-aware region enhancement and leakage region segmentation. In the first stage, YOLO-Fastestv2 is employed to capture the Region of Interest (ROI), which reduces redundancy computations. Additionally, we propose image enhancement to relieve the impact of uneven illumination, enhancing the texture details of the ROI. In the second stage, we propose a novel feature extraction network. Specifically, we propose the multi-scale feature fusion module (MFFM) and the Sequential Self-Attention Mechanism (SSAM) to capture discriminative representations of leakage. The multi-level features are fused by the MFFM with a small number of parameters, which capture leakage semantics at different scales. The SSAM realizes the enhancement of valid features and the suppression of invalid features by the adaptive weighting of spatial and channel dimensions. Furthermore, we generate a self-built dataset of sauce packets, including 606 images with various leakage areas. Comprehensive experiments demonstrate that our ISLS method shows better results than several state-of-the-art methods, with additional performance analyses deployed on intelligent sensors to affirm the effectiveness of our proposed method.

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Section: Comparison With Other Segmentation Methodsmentioning

confidence: 99%

ISLS: An Illumination-Aware Sauce-Packet Leakage Segmentation Method

You,

Lin,

Feng

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…The contour features of the image are obtained by employing an edge detection algorithm [29]. Commonly employed edge detection operators are the Sobel operator, which is a first-order differential operator, the DoG operator, which is a second-order operator, and the Canny operator.…”

Section: Feature Extractionmentioning

confidence: 99%

Real-Time Cucumber Target Recognition in Greenhouse Environments Using Color Segmentation and Shape Matching

Liu,

Sun,

Xia

et al. 2024

Applied Sciences

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Accurate identification of fruits in greenhouse environments is an essential need for the precise functioning of agricultural robots. This study presents a solution to the problem of distinguishing cucumber fruits from their stems and leaves, which often have similar colors in their natural environment. The proposed algorithm for cucumber fruit identification relies on color segmentation and form matching. First, we get the boundary details from the acquired image of the cucumber sample. The edge information is described and reconstructed by utilizing a shape descriptor known as the Fourier descriptor in order to acquire a matching template image. Subsequently, we generate a multi-scale template by amalgamating computational and real-world data. The target image is subjected to color conditioning in order to enhance the segmenacktation of the target region inside the HSV color space. Then, the segmented target region is compared to the multi-scale template based on its shape. The method of color segmentation decreases the presence of unwanted information in the target image, hence improving the effectiveness of shape matching. An analysis was performed on a set of 200 cucumber photos that were obtained from the field. The findings indicate that the method presented in this study surpasses conventional recognition algorithms in terms of accuracy and efficiency, with a recognition rate of up to 86%. Moreover, the system has exceptional proficiency in identifying cucumber targets within greenhouses. This attribute renders it a great resource for offering technical assistance to agricultural robots that operate with accuracy.

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Mining Abnormal Patterns in Moving Target Trajectories Based on Multi-Attribute Classification

Xie,

Guo,

Zheng

2024

Mathematics

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As a type of time series data, trajectory data objectively record the location information and corresponding time information of an object’s activities. It not only describes the spatial activity trajectory of a moving object but also contains the unique attributes, states, and behavioral characteristics of the moving object itself. It can also reflect the interaction relationship between the object’s activities and various elements in the environment to a certain extent. Therefore, mining from moving target trajectory data to discover implicit, effective, and potentially useful spatiotemporal behavior patterns of moving targets, such as anomaly detection, will have significant research significance. This paper proposes a method for mining abnormal patterns in the trajectory of moving targets based on multi-attribute classification. Firstly, to explore the activity location patterns of single moving targets, a frequent sequence discovery method for moving targets based on sequence patterns is proposed. Furthermore, for moving target trajectory data sets containing multiple attributes, numerical attributes are extracted, and the data are clustered according to attribute classification to extract a set of normal behavior patterns of moving targets. Then, combining the activity location patterns and normal behavior patterns of the moving target, the original trajectory data are compared with them to achieve the goal of detecting abnormal behavior of the moving target. Finally, an incremental anomaly detection scheme is proposed to address the characteristics of fast updates and large numbers of data in trajectory data sets. This involves synchronously updating the frequency of moving target activity patterns and the range of values for normal behavior patterns while updating the trajectory data set, in order to meet the needs of database updates and improve the accuracy and credibility of results.

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An algorithm for extracting similar segments of moving target trajectories based on shape matching

Cited by 3 publications

References 53 publications

ISLS: An Illumination-Aware Sauce-Packet Leakage Segmentation Method

ISLS: An Illumination-Aware Sauce-Packet Leakage Segmentation Method

Real-Time Cucumber Target Recognition in Greenhouse Environments Using Color Segmentation and Shape Matching

Mining Abnormal Patterns in Moving Target Trajectories Based on Multi-Attribute Classification

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