A human behavior analysis model to track object behavior in surveillance videos

Shruthi, .; Pattan, Prakash; Shravankumar,

doi:10.1016/j.measen.2022.100454

Cited by 4 publications

(1 citation statement)

References 6 publications

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“…Additionally, a large number of people travel daily through train stations, bus stations, metro stations, and airports. Controlling crowds and managing public meetings safely is crucial in these high-density crowd circumstances [11]. The common method employed all over the world is having a control room with various screens showing CCTV cameras live streams for crowd anomalies detection.…”

Section: Introductionmentioning

confidence: 99%

Abnormal Crowd Behaviour Detection in Surveillance Videos Using Spatiotemporal Inter-Fused Autoencoder

2023

IJIES

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In crowded environments, the importance of automatic video surveillance cannot be overstated. It plays a vital role in detecting unusual incidents and averting accidents, particularly in areas teeming with pedestrians. Surveillance frameworks demonstrate their inherent strength when deployed in real-world scenarios. In this paper, an efficient algorithm that can identify abnormalities in videos has been proposed as a solution to the problems of high computing power and variation between negative and positive samples. To address the issue of fewer negative samples, the algorithm employs the spatiotemporal inter-fused autoencoder in a method of unsupervised learning to locate and extract the samples of negative from the dataset. A spatial-temporal convolutional neural network (CNN) is built using this methodology, and it has a basic structure and requires minimal computing power. To develop the model of detection, the spatiotemporal CNN is trained using the method of supervised training with negative and positive data. The UMN and UCSD datasets are used as benchmark datasets for this method. The outcomes of the experiment demonstrate that the proposed method is more accurate than the existing algorithms at both frame and pixel levels and it can locate anomalous behaviors in real-time. The proposed method achieves better accuracy of 99.76%, 99.92%, 99.15%, 98.45%, and 94.67% in UCSD ped1, UCSD ped2, and UMN scene 1, 2, 3 datasets compared to the hybrid CNN and RF classifiers, MCMS-BCN attention+densenet121/Efficientnetv2, gradient motion descriptor (PGD) and enhanced entropy classifier, and attention mechanism. Additionally, the proposed method achieves better AUC of 99.96%, 99.83%, 99.97%, 90.15%, and 99.72% in the datasets of UCSD ped1, UCSD ped2, and UMN scenes 1, 2, 3 compared to the low-rank and compact coefficient dictionary learning (LRCCDL), and hybrid CNN and RF classifiers.

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Section: Introductionmentioning

confidence: 99%

Abnormal Crowd Behaviour Detection in Surveillance Videos Using Spatiotemporal Inter-Fused Autoencoder

2023

IJIES

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Multi-level glowworm swarm convolution neural networks for abnormal event detection in online surveillance video

Rao,

Ashok Kumar

2024

Int. j. inf. tecnol.

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Deep Custom Transfer Learning Models for Recognizing Human Activities via Video Surveillance

Gupta

Mahapatra

2023

Preprint

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The use of video surveillance for human activity recognition (HAR) in inpatient rehabilitation, activity recognition, or mobile health monitoring has grown in popularity recently. Before using it on new users, a HAR classifier is often trained offline with known users. If the activity patterns of new users differ from those in the training data, the accuracy of this method for them can be subpar. Because of the high cost of computing and the lengthy training period for new users, it is impractical to start from scratch when building mobile applications. The 2DCNNLSTM, Transfer 2DCNNLSTM, LRCN, or Transfer LRCN were proposed in this paper as deep learning and transfer learning models for recognizing human activities via video surveillance. The Transfer LRCN scored 100 for Training Accuracy and 69.39 for Validation Accuracy, respectively. The lowest Validation Loss of 0.16 and the Lowest Training Loss of 0.001 was obtained by Transfer LRCN, respectively. The 2DCNNLSTM has a 98.34 lowest training accuracy and a 47.62 lowest validation accuracy.

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A human behavior analysis model to track object behavior in surveillance videos

Cited by 4 publications

References 6 publications

Abnormal Crowd Behaviour Detection in Surveillance Videos Using Spatiotemporal Inter-Fused Autoencoder

Abnormal Crowd Behaviour Detection in Surveillance Videos Using Spatiotemporal Inter-Fused Autoencoder

Multi-level glowworm swarm convolution neural networks for abnormal event detection in online surveillance video

Deep Custom Transfer Learning Models for Recognizing Human Activities via Video Surveillance

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