Small-Scale Urban Object Anomaly Detection Using Convolutional Neural Networks with Probability Estimation

Author:

García-Aguilar Iván12ORCID,Luque-Baena Rafael Marcos12ORCID,Domínguez Enrique12ORCID,López-Rubio Ezequiel12ORCID

Affiliation:

1. Department of Computer Languages and Computer Science, University of Málaga, Bulevar Louis Pasteur 35, 29071 Málaga, Spain

2. Biomedical Research Institute of Málaga (IBIMA), C/Doctor Miguel Díaz Recio 28, 29010 Málaga, Spain

Abstract

Anomaly detection in sequences is a complex problem in security and surveillance. With the exponential growth of surveillance cameras in urban roads, automating them to analyze the data and automatically identify anomalous events efficiently is essential. This paper presents a methodology to detect anomalous events in urban sequences using pre-trained convolutional neural networks (CNN) and super-resolution (SR) models. The proposal is composed of two parts. In the offline stage, the pre-trained CNN model evaluated a large dataset of urban sequences to detect and establish the common locations of the elements of interest. Analyzing the offline sequences, a density matrix is calculated to learn the spatial patterns and identify the most frequent locations of these elements. Based on probabilities previously calculated from the offline analysis, the pre-trained CNN, now in an online stage, assesses the probability of anomalies appearing in the real-time sequence using the density matrix. Experimental results demonstrate the effectiveness of the presented approach in detecting several anomalies, such as unusual pedestrian routes. This research contributes to urban surveillance by providing a practical and reliable method to improve public safety in urban environments. The proposed methodology can assist city management authorities in proactively detecting anomalies, thus enabling timely reaction and improving urban safety.

Funder

Autonomous Government of Andalusia

Ministry of Science and Innovation of Spain

European Regional Development Fund

University of Málaga

SCBI (Supercomputing and Bioinformatics) center of the University of Málaga

NVIDIA Corporation

Universidad de Málaga

Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina-IBIMA Plataforma BIONAND

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

Reference22 articles.

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2. Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., and Tian, Q. (2019). CenterNet: Keypoint Triplets for Object Detection. arXiv.

3. Tan, M., and Le, Q.V. (2020). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. arXiv.

4. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016). Computer Vision—ECCV 2016, Proceedings of the 14th European Conference, Amsterdam, The Netherlands, 11–14 October 2016, Springer International Publishing.

5. Improved detection of small objects in road network sequences using CNN and super resolution;Expert Syst.,2021

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