Machine Learning Models for Predicting Crime Hotspots in Urban Areas Using Video Surveillance

Authors: Ravikanth Konda

DOI: https://doi.org/10.37082/IJIRMPS.v8.i5.232456

Short DOI: https://doi.org/g9hm86

Country: USA

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Abstract: In the smart city era, the roll-out of intelligent surveillance systems is fast becoming a foundation for urban security and law enforcement. Conventional crime analysis techniques tend to be backward-looking and heavily dependent on static information sources like past crime histories, census data, and socio-economic profiles at the neighborhood level. Such techniques, though beneficial, are not agile or granular enough to meet the demands of dynamic urban conditions. The emergence of machine learning (ML) and computer vision has created new avenues in predictive policing such that the authorities can predict crime based on real-time information.
This research paper explores the incorporation of machine learning algorithms with video monitoring systems to forecast crime hotspots in city areas. By extracting behavioral and environmental features from video feeds and integrating them with geospatial and temporal data, we lay down a robust framework for identifying hot spots at risk of criminal activity. Various ML algorithms such as logistic regression, support vector machines, random forests, and deep learning models such as convolutional neural networks (CNNs) and long short-term memory (LSTM) networks were trained and evaluated on synthesized datasets based on publicly available surveillance videos, open-source crime records, and simulated behavioral labels.
Experimental outcomes reveal that video-derived feature-based models are always more accurate in predictions than conventional data-only models, and combinations of CNN-LSTM rates attain an accuracy rate of well over 90%. Results highlight the promise of AI-augmented surveillance in helping build proactive crime prevention. The paper also addresses practical considerations for deployment, challenges like privacy, and future work towards enhancing transparency and accountability in predictive policing systems.

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Paper Id: 232456

Published On: 2020-10-08

Published In: Volume 8, Issue 5, September-October 2020

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