Suspicious Behavior Detection on Shoplifting Cases for Crime Prevention by Using 3D Convolutional Neural Networks

• URL: http://arxiv.org/abs/2005.02142v1
• Date: Thu, 30 Apr 2020 22:06:16 GMT
• Title: Suspicious Behavior Detection on Shoplifting Cases for Crime Prevention by Using 3D Convolutional Neural Networks
• Authors: Guillermo A. Mart\'inez-Mascorro, Jos\'e R. Abreu-Pederzini, Jos\'e C. Ortiz-Bayliss, Hugo Terashima-Mar\'in
• Abstract summary: We implement a 3DCNN model as a video feature extractor and tested its performance on a dataset composed of daily-action and shoplifting samples. The results are encouraging since it correctly identifies 75% of the cases where a crime is about to happen.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Crime generates significant losses, both human and economic. Every year, billions of dollars are lost due to attacks, crimes, and scams. Surveillance video camera networks are generating vast amounts of data, and the surveillance staff can not process all the information in real-time. The human sight has its limitations, where the visual focus is among the most critical ones when dealing with surveillance. A crime can occur in a different screen segment or on a distinct monitor, and the staff may not notice it. Our proposal focuses on shoplifting crimes by analyzing special situations that an average person will consider as typical conditions, but may lead to a crime. While other approaches identify the crime itself, we instead model suspicious behavior -- the one that may occur before a person commits a crime -- by detecting precise segments of a video with a high probability to contain a shoplifting crime. By doing so, we provide the staff with more opportunities to act and prevent crime. We implemented a 3DCNN model as a video feature extractor and tested its performance on a dataset composed of daily-action and shoplifting samples. The results are encouraging since it correctly identifies 75% of the cases where a crime is about to happen.

Related papers

• A Data-Theoretic Approach to Identifying Violent Facial Expressions in Social Crime Contexts [0.0]
  We have designed an automated system by using a Convolutional Neural Network which can detect whether a person has any intention to commit any crime. Here we used only the facial data of a specific geographic region which can represent the violent and before-crime before-crime facial patterns of the people of the whole region.
  arXiv  Detail & Related papers  (2023-08-16T20:12:43Z)
• Ranking the locations and predicting future crime occurrence by retrieving news from different Bangla online newspapers [2.3979112009934163]
  We have come up with an approach which can give an approximation to people about the safety of a specific location. Our approach relies on different online Bangla newspapers for crawling the crime data.
  arXiv  Detail & Related papers  (2023-05-18T04:19:26Z)
• A Comparative Analysis of Multiple Methods for Predicting a Specific Type of Crime in the City of Chicago [0.9799637101641152]
  We aim to answer the question: "How well can we predict theft using spatial and temporal features?" XBoost showed the best results with an F1-score of 0.86.
  arXiv  Detail & Related papers  (2023-04-26T11:35:06Z)
• CCTV-Gun: Benchmarking Handgun Detection in CCTV Images [59.24281591714385]
  Gun violence is a critical security problem, and it is imperative for the computer vision community to develop effective gun detection algorithms. detecting guns in real-world CCTV images remains a challenging and under-explored task. We present a benchmark, called textbfCCTV-Gun, which addresses the challenges of detecting handguns in real-world CCTV images.
  arXiv  Detail & Related papers  (2023-03-19T16:17:35Z)
• Criminal Investigation Tracker with Suspect Prediction using Machine Learning [0.0]
  This study provides a novel approach for crime prediction based on real-world data, and criminality incorporation. An automated approach to identifying offenders in Sri Lanka would be better than the current system.
  arXiv  Detail & Related papers  (2023-02-21T03:24:17Z)
• Untargeted Backdoor Attack against Object Detection [69.63097724439886]
  We design a poison-only backdoor attack in an untargeted manner, based on task characteristics. We show that, once the backdoor is embedded into the target model by our attack, it can trick the model to lose detection of any object stamped with our trigger patterns.
  arXiv  Detail & Related papers  (2022-11-02T17:05:45Z)
• Initiative Defense against Facial Manipulation [82.96864888025797]
  We propose a novel framework of initiative defense to degrade the performance of facial manipulation models controlled by malicious users. We first imitate the target manipulation model with a surrogate model, and then devise a poison perturbation generator to obtain the desired venom.
  arXiv  Detail & Related papers  (2021-12-19T09:42:28Z)
• The effect of differential victim crime reporting on predictive policing systems [84.86615754515252]
  We show how differential victim crime reporting rates can lead to outcome disparities in common crime hot spot prediction models. Our results suggest that differential crime reporting rates can lead to a displacement of predicted hotspots from high crime but low reporting areas to high or medium crime and high reporting areas.
  arXiv  Detail & Related papers  (2021-01-30T01:57:22Z)
• Measurement-driven Security Analysis of Imperceptible Impersonation Attacks [54.727945432381716]
  We study the exploitability of Deep Neural Network-based Face Recognition systems. We show that factors such as skin color, gender, and age, impact the ability to carry out an attack on a specific target victim. We also study the feasibility of constructing universal attacks that are robust to different poses or views of the attacker's face.
  arXiv  Detail & Related papers  (2020-08-26T19:27:27Z)
• DFraud3- Multi-Component Fraud Detection freeof Cold-start [50.779498955162644]
  The Cold-start is a significant problem referring to the failure of a detection system to recognize the authenticity of a new user. In this paper, we model a review system as a Heterogeneous InformationNetwork (HIN) which enables a unique representation to every component. HIN with graph induction helps to address the camouflage issue (fraudsterswith genuine reviews) which has shown to be more severe when it is coupled with cold-start, i.e., new fraudsters with genuine first reviews.
  arXiv  Detail & Related papers  (2020-06-10T08:20:13Z)
• Crime Prediction Using Spatio-Temporal Data [8.50468505606714]
  Supervised learning technique is used to predict crimes with better accuracy. The proposed system is feed with a criminal-activity data set of twelve years of San Francisco city.
  arXiv  Detail & Related papers  (2020-03-11T16:19:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.