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Anomaly Detection in Urban Areas

Abdulrahman Alreshidi

University of Ha’il, Ha’il, Saudi Arabia

Hina Afridi

COMSATS University, Wah Cantt, Pakistan

Wilayat Khan

COMSATS University, Wah Cantt, Pakistan

1-6

Vol: 9, Issue: 1, 2019

Receiving Date: 2018-11-17 Acceptance Date:

2019-01-08

Publication Date:

2019-01-12

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Abstract

In this paper, we propose a novel approach to detect anomalies in urban areas. This is achieved by analyzing the crowd behavior by extracting the local binary patterns (LBP) and Laplacian of Gaussian (LoG) features. We integrate both features together. These features are used to train an MLP neural network during the training stage, and the anomaly is inferred on the test samples. Considering the difficulty of tracking individuals in dense crowds due to multiple occlusions and clutter. In this work, we extract LBP and LoG features and consider them as an approximate representation of the anomaly. These features well match the appearance of anomaly and their consistency, and accuracy is higher both in structured and unstructured urban areas compared to other detectors. In the current work, these features are used as input prior to train the neural network. The MLP neural network is subsequently used to highlight these features that can reveal the anomaly. The experimental evaluation is conducted on a set of benchmark video sequences commonly used for anomaly detection. replica uhren Forever perfect UK fake watches for males and females.
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Keywords: Local binary pattern; Laplacian of Gaussian; Anomaly; Motion

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