Correlating Belongings with Passengers in a Simulated Airport Security Checkpoint

Automatic algorithms for tracking and associating passengers and their divested objects at an airport security screening checkpoint would have great potential for improving checkpoint efficiency, including flow analysis, theft detection, line-of-sight maintenance, and risk-based screening. In this paper, we present algorithms for these tracking and association problems and demonstrate their effectiveness in a full-scale physical simulation of an airport security screening checkpoint. Our algorithms leverage both hand-crafted and deep-learning-based approaches for passenger and bin tracking, and are able to accurately track and associate objects through a ceiling-mounted multicamera array. We validate our algorithm on ground-truthed datasets collected at the simulated checkpoint that reflect natural passenger behavior, achieving high rates of passenger/object/transfer event detection while maintaining low false alarm and mismatch rates.

• Publication year

  2018

• Venue

  ACM/IEEE International Conference on Distributed Smart Cameras

• Publication date

  2018-09-03

• Fields of study

  Computer Science, Engineering

• Identifiers
  DOI 10.1145/3243394.3243703
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Abandoned Object Detection via Temporal Consistency Modeling and Back-Tracing Verification for Visual Surveillance

  2018cited by this paper
• Learning Background-Aware Correlation Filters for Visual Tracking

  2017influential reference
• Realtime Multi-person 2D Pose Estimation Using Part Affinity Fields

  2016cited by this paper
• Best-Buddies Similarity for robust template matching

  2015cited by this paper
• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

  2015cited by this paper
• Best-Buddies Similarity for robust template matching

  2015cited by this paper
• Very Deep Convolutional Networks for Large-Scale Image Recognition

  2014cited by this paper
• Object tracking across non-overlapping views by learning inter-camera transfer models

  2014cited by this paper
• Caffe: Convolutional Architecture for Fast Feature Embedding

  2014cited by this paper
• Microsoft COCO: Common Objects in Context

  2014cited by this paper
• Real-time airport security checkpoint surveillance using a camera network

  2011cited by this paper
• An Abandoned Object Detection System Based on Dual Background Segmentation

  2009cited by this paper
• ImageNet: A large-scale hierarchical image database

  2009cited by this paper
• Robust Unattended and Stolen Object Detection by Fusing Simple Algorithms

  2008cited by this paper
• Automated multi-camera planar tracking correspondence modeling

  2003cited by this paper
• Universität Des Saarlandes Fachrichtung 6.1 – Mathematik Lucas/kanade Meets Horn/schunck: Combining Local and Global Optic Flow Methods Lucas/kanade Meets Horn/schunck: Combining Local and Global Optic Flow Methods

  year unknowncited by this paper

• Advancements in machine learning techniques for threat item detection in X-ray images: a comprehensive survey

  2024cites this paper
• Towards Automatic Threat Detection: A Survey of Advances of Deep Learning within X-ray Security Imaging

  2020cites this paper
• Tracking Passengers and Baggage Items Using Multiple Overhead Cameras at Security Checkpoints

  2020cites this paper
• A novel enhanced region proposal network and modified loss function: threat object detection in secure screening using deep learning

  2020cites this paper
• Single-Frequency Fast Dielectric Characterization of Concealed Body-Worn Explosive Threats

  2020cites this paper
• Tracking Passengers and Baggage Items using Multi-camera Systems at Security Checkpoints

  2020cites this paper
• ALGORITHMIC FRAMEWORK TO TRACK OBJECTS AND EVENTS

  2019cites this paper
• A Collaborative Algorithmic Framework to Track Objects And Events

  2019cites this paper