Integrating Structural Symmetry and Local Homoplasy Information in Intuitionistic Fuzzy Clustering for Infrared Pedestrian Segmentation

Jin, Darui; Bai, Xiangzhi; Wang, Yingfan

doi:10.1109/tsmc.2019.2931699

Cited by 10 publications

(7 citation statements)

References 52 publications

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“…The Kanade Lucas-Tomasi (KLT) uses corner points to display moving people and cluster motion information elements in a controlled setting, is cited in [39]. To find anomalies in a scene, the author used two different kinds of historical and self-history descriptors, as well as histories of nearby objects [40].…”

Section: Suspicious Activity Detectionmentioning

confidence: 99%

Novel Pedestrian Detection and Suspicious Activity Recognition Using Enhanced YOLOv5 and Motion Feature Map

Gawande¹,

Hajari²,

Golhar

2023

Preprint

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Pedestrian detection and suspicious activity recognition are notable challenges in vision-based surveillance systems. However, the accuracy of pedestrian detection is influenced by a wide range of factors, including human presence, trajectory, posture, complex background, and object deformation. In this paper, we developed a pedestrian dataset that encompasses student behavior in an institution, such as cheating, stealing lab devices, dispute, and threatening scenarios. It provides uniform and steady identification annotations of pedestrians, making it suitable for pedestrian detection, tracking, and behavior detection. In addition, we also improved detection accuracy using the enhanced YOLOv5 architecture. Next, we proposed an efficient method for detection of global and local unusual behaviors. The proposed method extracts motion features that accurately describe pedestrian motion, speed, and direction, etc. The proposed method is validated on a proposed student behavior database and three additional publicly available benchmark datasets. It gives state-of-the-art detection accuracy of 96.12% and miss rate of 6.68% compared to the existing techniques. The experimental results demonstrate a substantial performance improvement in anomalous activity recognition. The final section summarizes and discusses future research directions.

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Section: Suspicious Activity Detectionmentioning

confidence: 99%

Novel Pedestrian Detection and Suspicious Activity Recognition Using Enhanced YOLOv5 and Motion Feature Map

Gawande¹,

Hajari²,

Golhar

2023

Preprint

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“…Currently, visual quality inspection is carried out by defect detection using supervised or unsupervised methods to obtain defect type and defect localization. Common supervised methods are object detection [53] and semantic segmentation [54] methods. Major unsupervised method is anomaly detection [55], [56], [57], also known as outlier detection [58] or novelty detection [59].…”

Section: B Predictive Intelligencementioning

confidence: 99%

Integrated Inspection on PCB Manufacturing in Cyber–Physical–Social Systems

Wang

Cao

et al. 2023

IEEE Trans. Syst. Man Cybern, Syst.

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The printed circuit boards (PCBs) industry is one of the fastest-growing industries in recent decades. The PCB manufacturing process is highly complicated and severely affected by social factors, which makes it very important to conduct integrated inspection, assuring and improving the production quality. In this article, we propose an artificial systems, computational experiments, and parallel execution-based integrated inspection method in cyber-physical-social systems (CPSS) to realize smart manufacturing. In this inspection system, rather than simply performing modeling, analysis, and control, we perform descriptive intelligence to construct production processes with limited multimodal information, perform predictive intelligence to conduct defect detection and defect prediction, and perform prescriptive intelligence to achieve defect diagnosis and defect management. In this way, our inspection system could offer a learning and training platform for workers to master professional inspection skills, provide an experimentation and evaluation platform for product defect monitoring and early warnings, and supply guidance about defect management and control to improve manufacturing processes. For technical implementation, we leverage a Transformer-based foundation model to achieve knowledge reasoning and human-computer interaction. As a result, we provide an innovative solution to cope with the challenges of quality inspection in current smart manufacturing, and expect its further applications in the PCB industry.

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“…Other recent research groups have concentrated on moving pedestrian motion orientation and speed information. The Kanade Lucas-Tomasi (KLT) approach [9] is employed by Zhang et al [19], in which corner points are used to display pedestrians that are moving and cluster the motion information features in the controlled environment. The author used two types of historical and self-history descriptors, as well as neighbouring object histories, to detect abnormalities in a scene [10].…”

Section: Literature Surveymentioning

confidence: 99%

“…Many authors have attempted to detect abnormal behaviour in overcrowded environments using texturebased information, such as time gradients [4], dynamic texture characteristics [5] and the spatiotemporal frequency properties [6], [7]. Other groups concentrate on optical flows, which recognize motion features in video frames directly, such as multi-scale pedestrian features [8], fuzzy clustering based features [9], behavioural model for pedestrian detection [10], convolutional neural networks (CNN) features [11], weighted autoencoder based features [12], trajectory based features [13], student object behavioral features [14], multi-target association based features [15], [16]. Previous research has shown that the technique of motion is beneficial, and we believe that the present methods can still be improved.…”

Section: Introductionmentioning

confidence: 99%

Novel approach for pedestrian unusual activity detection in academic environment

Hajari

Gawande

Golhar

2022

IJ-AI

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<span>In this paper, we propose an efficient method for the detection of student unusual activity in the academic environment. The proposed method extracts motion features that accurately describe the motion characteristics of the pedestrian's movement, velocity, and direction, as well as their intercommunication within a frame. We also use these motion features to detect both global and local anomalous behaviors within the frame. The proposed approach is validated on a newly built proposed student behavior database and three additional publicly available benchmark datasets. When compared to state-of-the-art techniques, the experimental results reveal a considerable performance improvement in anomalous activity recognition. Finally, we summarize and discuss future research directions.</span>

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Integrating Structural Symmetry and Local Homoplasy Information in Intuitionistic Fuzzy Clustering for Infrared Pedestrian Segmentation

Cited by 10 publications

References 52 publications

Novel Pedestrian Detection and Suspicious Activity Recognition Using Enhanced YOLOv5 and Motion Feature Map

Novel Pedestrian Detection and Suspicious Activity Recognition Using Enhanced YOLOv5 and Motion Feature Map

Integrated Inspection on PCB Manufacturing in Cyber–Physical–Social Systems

Novel approach for pedestrian unusual activity detection in academic environment

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