Abnormal Crowd Behavior Detection Using Optimized Pyramidal Lucas-Kanade Technique

Rajasekaran, G.; Sekar, J. Raja

doi:10.32604/iasc.2023.029119

Cited by 1 publication

(1 citation statement)

References 32 publications

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“…The comparative analysis of residual attentionbased LSTM with SGReLU by metrics like accuracy, precision, recall, f1-score, and AUC as shown in Table 5. The existing result such as [16][17][18][19][20][21][22]…”

Section: Comparative Analysismentioning

confidence: 99%

Residual Attention based Long-Short Term Memory with Self Gated Rectified Linear Unit for Anomalous Behavior Detection

2024

IJIES

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The detection of anomalous behavior in video surveillance is a focus of present research which has huge value and extensive application probabilities. Due to the difficulty of human movement and the feasibility of environments, anomalous behavior detection has some challenges. This paper proposed a deep learning-based approach for detecting the weapon and anomalous behavior. The Residual Attention-based Long-Short Term Memory (LSTM) with Self Gated Rectified Linear Unit (SGReLU) is proposed to enhance the detection accuracy. The median filter is used for preprocessing which removes noise from the UCF-Crime dataset and feeds into Histogram of Oriented Gradients (HOG) for feature extraction. Then, the Reverse Learning Chimp Optimization Algorithm (RL-COA) is utilized for hyperparameter optimization which attains the individual's reverse solution and then preserves the individual with high fitness values. At last, the Residual Attention-based LSTM with SGReLU is utilized for the classification process. This model overcomes neuron dead issues by allowing negative values for some neurons and minimizing the probability of inactive neurons. The proposed model attained better results on UCF-Crime dataset through the metrics like accuracy, precision, recall, f1-score and AUC values of about 98.84%, 98.62%, 98.47%, 98.35% and 98.21% correspondingly that ensures accurate detection when compared to existing techniques such as ResNet18 with Simple Recurrent Unit (SRU), Residual attention-based LSTM and Convolutional LSTM.

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Section: Comparative Analysismentioning

confidence: 99%

Residual Attention based Long-Short Term Memory with Self Gated Rectified Linear Unit for Anomalous Behavior Detection

2024

IJIES

View full text Add to dashboard Cite

show abstract

Abnormal Crowd Behavior Detection Using Optimized Pyramidal Lucas-Kanade Technique

Cited by 1 publication

References 32 publications

Residual Attention based Long-Short Term Memory with Self Gated Rectified Linear Unit for Anomalous Behavior Detection

Residual Attention based Long-Short Term Memory with Self Gated Rectified Linear Unit for Anomalous Behavior Detection

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