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.
