Audio Analytics-based Human Trafficking Detection Framework for Autonomous Vehicles

• URL: http://arxiv.org/abs/2209.04071v1
• Date: Fri, 9 Sep 2022 01:06:50 GMT
• Title: Audio Analytics-based Human Trafficking Detection Framework for Autonomous Vehicles
• Authors: Sagar Dasgupta, Kazi Shakib, Mizanur Rahman, Silvana V Croope, Steven Jones
• Abstract summary: This study aims to develop an innovative audio analytics-based human trafficking detection framework for autonomous vehicles. We create a new and comprehensive audio dataset related to human trafficking with five classes i.e., crying, screaming, car door banging, car noise, and conversation. Our analyses reveal that the deep 1-D CNN can distinguish sound coming from a human trafficking victim from a non-human trafficking sound with an accuracy of 95%.
• Score: 2.868643768911536
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Human trafficking is a universal problem, persistent despite numerous efforts to combat it globally. Individuals of any age, race, ethnicity, sex, gender identity, sexual orientation, nationality, immigration status, cultural background, religion, socioeconomic class, and education can be a victim of human trafficking. With the advancements in technology and the introduction of autonomous vehicles (AVs), human traffickers will adopt new ways to transport victims, which could accelerate the growth of organized human trafficking networks, which can make the detection of trafficking in persons more challenging for law enforcement agencies. The objective of this study is to develop an innovative audio analytics-based human trafficking detection framework for autonomous vehicles. The primary contributions of this study are to: (i) define four non-trivial, feasible, and realistic human trafficking scenarios for AVs; (ii) create a new and comprehensive audio dataset related to human trafficking with five classes i.e., crying, screaming, car door banging, car noise, and conversation; and (iii) develop a deep 1-D Convolution Neural Network (CNN) architecture for audio data classification related to human trafficking. We have also conducted a case study using the new audio dataset and evaluated the audio classification performance of the deep 1-D CNN. Our analyses reveal that the deep 1-D CNN can distinguish sound coming from a human trafficking victim from a non-human trafficking sound with an accuracy of 95%, which proves the efficacy of our framework.

Related papers

• Tracing the Unseen: Uncovering Human Trafficking Patterns in Job Listings [9.450459784653196]
  We analyze over a quarter million job postings collected from eight relevant regions across the United States, spanning nearly two decades (2006-2024) Our investigation into the types of advertised opportunities, the modes of preferred contact, and the frequency of postings uncovers the patterns characterizing suspicious ads. This research underscores the imperative for a deeper dive into how online job boards and communication platforms could be unwitting facilitators of human trafficking.
  arXiv  Detail & Related papers  (2024-06-18T10:18:15Z)
• Creating Geospatial Trajectories from Human Trafficking Text Corpora [0.0]
  We propose a Narrative to Trajectory (N2T) information extraction system. N2T analyzes reported narratives, extracts relevant information through the use of Natural Language Processing (NLP) techniques, and applies geospatial augmentation. We evaluate N2T on human trafficking text corpora and demonstrate that our approach of utilizing data preprocessing and augmenting database techniques with NLP libraries outperforms existing geolocation detection methods.
  arXiv  Detail & Related papers  (2024-05-09T22:24:09Z)
• Learning Human Action Recognition Representations Without Real Humans [66.61527869763819]
  We present a benchmark that leverages real-world videos with humans removed and synthetic data containing virtual humans to pre-train a model. We then evaluate the transferability of the representation learned on this data to a diverse set of downstream action recognition benchmarks. Our approach outperforms previous baselines by up to 5%.
  arXiv  Detail & Related papers  (2023-11-10T18:38:14Z)
• A New Task and Dataset on Detecting Attacks on Human Rights Defenders [68.45906430323156]
  We propose a new dataset for detecting Attacks on Human Rights Defenders (HRDsAttack) consisting of crowdsourced annotations on 500 online news articles. The annotations include fine-grained information about the type and location of the attacks, as well as information about the victim(s) We demonstrate the usefulness of the dataset by using it to train and evaluate baseline models on several sub-tasks to predict the annotated characteristics.
  arXiv  Detail & Related papers  (2023-06-30T14:20:06Z)
• Human-Centric Multimodal Machine Learning: Recent Advances and Testbed on AI-based Recruitment [66.91538273487379]
  There is a certain consensus about the need to develop AI applications with a Human-Centric approach. Human-Centric Machine Learning needs to be developed based on four main requirements: (i) utility and social good; (ii) privacy and data ownership; (iii) transparency and accountability; and (iv) fairness in AI-driven decision-making processes. We study how current multimodal algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data.
  arXiv  Detail & Related papers  (2023-02-13T16:44:44Z)
• Fragments of the Past: Curating Peer Support with Perpetrators of Domestic Violence [88.37416552778178]
  We report on a ten-month study where we worked with six support workers and eighteen perpetrators in the design and deployment of Fragments of the Past. We share how crafting digitally-augmented artefacts - 'fragments' - of experiences of desisting from violence can translate messages for motivation and rapport between peers. These insights provide the basis for practical considerations for future network design with challenging populations.
  arXiv  Detail & Related papers  (2021-07-09T22:57:43Z)
• CCC/Code 8.7: Applying AI in the Fight Against Modern Slavery [3.2013947720960267]
  Human trafficking occurs when a trafficker compels someone to provide labor or services through the use of force, fraud, and/or coercion. The wide range of stakeholders in human trafficking presents major challenges. Technology that increase surveillance of populations can also undermine basic human rights.
  arXiv  Detail & Related papers  (2021-06-24T17:07:56Z)
• Operations Research and Analytics to Combat Human Trafficking: A Systematic Review of Academic Literature [0.0]
  Human trafficking is a widespread and compound social, economic, and human rights issue occurring in every region of the world. There have been an increasing number of anti-human trafficking works from the Operations Research and Analytics domains in recent years. We fill this gap by providing a systematic literature review that identifies and classifies the body of Operations Research and Analytics research related to the anti-human trafficking domain.
  arXiv  Detail & Related papers  (2021-03-30T16:32:57Z)
• Fine-Grained Vehicle Perception via 3D Part-Guided Visual Data Augmentation [77.60050239225086]
  We propose an effective training data generation process by fitting a 3D car model with dynamic parts to vehicles in real images. Our approach is fully automatic without any human interaction. We present a multi-task network for VUS parsing and a multi-stream network for VHI parsing.
  arXiv  Detail & Related papers  (2020-12-15T03:03:38Z)
• Learning Accurate and Human-Like Driving using Semantic Maps and Attention [152.48143666881418]
  This paper investigates how end-to-end driving models can be improved to drive more accurately and human-like. We exploit semantic and visual maps from HERE Technologies and augment the existing Drive360 dataset with such. Our models are trained and evaluated on the Drive360 + HERE dataset, which features 60 hours and 3000 km of real-world driving data.
  arXiv  Detail & Related papers  (2020-07-10T22:25:27Z)

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.