Chaotic whale-atom search optimization-based deep stacked auto encoder for crowd behaviour recognition

The activity recognition gained immense popularity due to increasing number of surveillance cameras. The purpose of activity recognition is to detect the actions from the series of examination by varying the environmental condition. In this paper, Chaotic Whale Atom Search Optimisation (CWASO)-based...

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Bibliographic Details
Published in:Journal of experimental & theoretical artificial intelligence Vol. 36; no. 2; pp. 187 - 211
Main Authors: Singh, Juginder Pal, Kumar, Manoj
Format: Journal Article
Language:English
Published: Abingdon Taylor & Francis 17.02.2024
Taylor & Francis Ltd
Subjects:
Activity recognition
Algorithms
Coders
crowd behaviour
Crowd monitoring
Datasets
deep stacked autoencoder
Feature recognition
LOOP
Optical flow (image analysis)
Optimization
Searching
Sensitivity
statistical features
Video surveillance
Whales
ISSN:0952-813X, 1362-3079
Online Access:Get full text
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Summary:The activity recognition gained immense popularity due to increasing number of surveillance cameras. The purpose of activity recognition is to detect the actions from the series of examination by varying the environmental condition. In this paper, Chaotic Whale Atom Search Optimisation (CWASO)-based Deep stacked autoencoder (CWASO-Deep SAE) is proposed for crowd behaviour recognition. The key frames are subjected to the descriptor of feature to extort the features, which bring out the classifier input vector. In this model, the statistical features, optical flow features and visual features are conducted to extract important features. Furthermore, the significant features are shown in the deep stacked auto-encoder (Deep SAE) for activity recognition, as the guidance of deep SAE is performed byCWASO, that is planned is designed by adjoining Atom search optimisation (ASO) algorithm and Chaotic Whale optimisation algorithm (CWOA). The proposed systems' performance is analysed using two datasets. By considering the training data, the projected method attains performance that is high for dataset-1 with maximum precision, sensitivity, and with specific value of 96.826%, 96.790%, and 99.395%, respectively. Similarly, by considering the K-Fold, this method attains the maximum precision of 96.897%, sensitivity of 96.885%, and with specific values of 97.245% for the dataset-1.
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ISSN:0952-813X
1362-3079
DOI:10.1080/0952813X.2022.2084566