Detection of Appearance and Behavior Anomalies in Stationary Camera Videos Using Convolutional Neural Networks

Chen, H.; Bohush, Rykhard; Kurnosov, I.; Ma, GuoQing; Weichen, Yang; Ablameyko, Sergey

doi:10.1134/s1054661822020067

Cited by 6 publications

(1 citation statement)

References 17 publications

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“…As depicted in Figure 9, the recognition accuracy of FrCMs-DNNs is higher than FrCMIs, and the FrCMs-DNNs model has the best classification results. Figure 10 presents the confusion matrix of the fractional Chebyshev moments models for the PSB dataset [35] and the medical images dataset [36]. Most of the confused objects are nearly completely recognized, with a little amount of confusion between the bird/airplane and ant/octopus categories in the PSB dataset, and a small amount of confusion between the abdomen/ hip and knee/leg categories in the medical images dataset, since these categories have similar shapes.…”

Section: D Object Recognitionmentioning

confidence: 99%

Recognition of 3D Images by Fusing Fractional-Order Chebyshev Moments and Deep Neural Networks

Gao,

Zhang,

Zhao

et al. 2024

Sensors

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In order to achieve efficient recognition of 3D images and reduce the complexity of network parameters, we proposed a novel 3D image recognition method combining deep neural networks with fractional-order Chebyshev moments. Firstly, the fractional-order Chebyshev moment (FrCM) unit, consisting of Chebyshev moments and the three-term recurrence relation method, is calculated separately using successive integrals. Next, moment invariants based on fractional order and Chebyshev moments are utilized to achieve invariants for image scaling, rotation, and translation. This design aims to enhance computational efficiency. Finally, the fused network embedding the FrCM unit (FrCMs-DNNs) extracts depth features to analyze the effectiveness from the aspects of parameter quantity, computing resources, and identification capability. Meanwhile, the Princeton Shape Benchmark dataset and medical images dataset are used for experimental validation. Compared with other deep neural networks, FrCMs-DNNs has the highest accuracy in image recognition and classification. We used two evaluation indices, mean square error (MSE) and peak signal-to-noise ratio (PSNR), to measure the reconstruction quality of FrCMs after 3D image reconstruction. The accuracy of the FrCMs-DNNs model in 3D object recognition was assessed through an ablation experiment, considering the four evaluation indices of accuracy, precision, recall rate, and F1-score.

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Section: D Object Recognitionmentioning

confidence: 99%

Recognition of 3D Images by Fusing Fractional-Order Chebyshev Moments and Deep Neural Networks

Gao,

Zhang,

Zhao

et al. 2024

Sensors

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Human pose feature enhancement for human anomaly detection and tracking

Nou,

Lee,

Ohyama

et al. 2024

Int. j. inf. tecnol.

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Human pose, represented as a set of keypoints, is a principal feature in pose-based human anomaly detection and tracking. However, using keypoint alone for both tasks encounter loss during heavy occlusion or missed keypoint detection, which leads to lower the model’s performance. To address these challenges, we propose a method that employs multi-object tracking as the tracker, incorporating human pose estimation to maintain robust tracking even when keypoint detection fails. Additionally, we introduce a pose selection module that selects the most appropriate pose and recovers the incomplete pose of each individual target. Accurately determining the most representative pose of each individual is crucial, as it enhances the precision of activity recognition and improves anomaly detection accuracy. Our pose selection module leverages various pose estimation models to generate diverse pose candidates for each tracked object, and then the similarity scores between those poses are computed to identify the most significant one. Our approach demonstrates improved performance, achieving an accuracy of up to 86.4%, surpassing state-of-the-art methods.

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Tracking People in Video Using Neural Network Features and Facial Identification Taking into Account the Mask Mode

Kurnosov

Bohush

et al. 2023

Pattern Recognit. Image Anal.

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Detection of Appearance and Behavior Anomalies in Stationary Camera Videos Using Convolutional Neural Networks

Cited by 6 publications

References 17 publications

Recognition of 3D Images by Fusing Fractional-Order Chebyshev Moments and Deep Neural Networks

Recognition of 3D Images by Fusing Fractional-Order Chebyshev Moments and Deep Neural Networks

Human pose feature enhancement for human anomaly detection and tracking

Tracking People in Video Using Neural Network Features and Facial Identification Taking into Account the Mask Mode

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