Identification of Anomalies in Images Using CNN and Autoencoders Techniques

Navya, V; Patil, Kavita Avinash

doi:10.1007/978-981-16-7118-0_26

Cited by 2 publications

(2 citation statements)

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“…Next to the detection of objects, the classification process is performed by the use of SAE model [19]. AE is a type of unsupervised learning infrastructure which maintains 3 layers such as input, hidden, and output layers.…”

Section: Object Classification Module: Sae Modelmentioning

confidence: 99%

“…AE is a type of unsupervised learning infrastructure which maintains 3 layers such as input, hidden, and output layers. The procedure of AE trained has 2 parts encoding and decoding [19]. The encoder was utilized to map an input dataset as to hidden representations, and decoding was mentioned that recreating input data in the hidden demonstration.…”

Section: Object Classification Module: Sae Modelmentioning

confidence: 99%

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Segmentation Based Real Time Anomaly Detection and Tracking Model for Pedestrian Walkways

Chitra¹

2023

Intelligent Automation &Amp; Soft Computing

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Presently, video surveillance is commonly employed to ensure security in public places such as traffic signals, malls, railway stations, etc. A major challenge in video surveillance is the identification of anomalies that exist in it such as crimes, thefts, and so on. Besides, the anomaly detection in pedestrian walkways has gained significant attention among the computer vision communities to enhance pedestrian safety. The recent advances of Deep Learning (DL) models have received considerable attention in different processes such as object detection, image classification, etc. In this aspect, this article designs a new Panoptic Feature Pyramid Network based Anomaly Detection and Tracking (PFPN-ADT) model for pedestrian walkways. The proposed model majorly aims to the recognition and classification of different anomalies present in the pedestrian walkway like vehicles, skaters, etc. The proposed model involves panoptic segmentation model, called Panoptic Feature Pyramid Network (PFPN) is employed for the object recognition process. For object classification, Compact Bat Algorithm (CBA) with Stacked Auto Encoder (SAE) is applied for the classification of recognized objects. For ensuring the enhanced results better anomaly detection performance of the PFPN-ADT technique, a comparison study is made using University of California San Diego (UCSD) Anomaly data and other benchmark datasets (such as Cityscapes, ADE20K, COCO), and the outcomes are compared with the Mask Recurrent Convolutional Neural Network (RCNN) and Faster Convolutional Neural Network (CNN) models. The simulation outcome demonstrated the enhanced performance of the PFPN-ADT technique over the other methods.

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Section: Object Classification Module: Sae Modelmentioning

confidence: 99%

Section: Object Classification Module: Sae Modelmentioning

confidence: 99%