Related papers: A Case Study of Counting the Number of Unique Users in Linear and Non-Linear Trails -- A Multi-Agent System Approach

A Case Study of Counting the Number of Unique Users in Linear and Non-Linear Trails -- A Multi-Agent System Approach

URL: http://arxiv.org/abs/2503.07651v1
Date: Thu, 06 Mar 2025 18:43:51 GMT
Title: A Case Study of Counting the Number of Unique Users in Linear and Non-Linear Trails -- A Multi-Agent System Approach
Authors: Tanvir Rahman,
Abstract summary: This study proposes a multi-agent system leveraging low-cost cameras in a distributed network to track and analyze unique users.<n>As a case study, we deployed this system at the Jack A. Markell (JAM) Trail in Wilmington, Delaware, and Hall Trail in Newark, Delaware.<n>The results demonstrate a 72% success rate in identifying unique users, setting a benchmark in automated park activity monitoring.
Score: 0.0
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Parks play a crucial role in enhancing the quality of life by providing recreational spaces and environmental benefits. Understanding the patterns of park usage, including the number of visitors and their activities, is essential for effective security measures, infrastructure maintenance, and resource allocation. Traditional methods rely on single-entry sensors that count total visits but fail to distinguish unique users, limiting their effectiveness due to manpower and cost constraints.With advancements in affordable video surveillance and networked processing, more comprehensive park usage analysis is now feasible. This study proposes a multi-agent system leveraging low-cost cameras in a distributed network to track and analyze unique users. As a case study, we deployed this system at the Jack A. Markell (JAM) Trail in Wilmington, Delaware, and Hall Trail in Newark, Delaware. The system captures video data, autonomously processes it using existing algorithms, and extracts user attributes such as speed, direction, activity type, clothing color, and gender. These attributes are shared across cameras to construct movement trails and accurately count unique visitors. Our approach was validated through comparison with manual human counts and simulated scenarios under various conditions. The results demonstrate a 72% success rate in identifying unique users, setting a benchmark in automated park activity monitoring. Despite challenges such as camera placement and environmental factors, our findings suggest that this system offers a scalable, cost-effective solution for real-time park usage analysis and visitor behavior tracking.

Related papers

Multi-modal Multi-platform Person Re-Identification: Benchmark and Method [58.59888754340054]
MP-ReID is a novel dataset designed specifically for multi-modality and multi-platform ReID. This benchmark compiles data from 1,930 identities across diverse modalities, including RGB, infrared, and thermal imaging. We introduce Uni-Prompt ReID, a framework with specific-designed prompts, tailored for cross-modality and cross-platform scenarios.
arXiv Detail & Related papers (2025-03-21T12:27:49Z)
Analysis of Unstructured High-Density Crowded Scenes for Crowd Monitoring [55.2480439325792]
We are interested in developing an automated system for detection of organized movements in human crowds.<n>Computer vision algorithms can extract information from videos of crowded scenes.<n>We can estimate the number of participants in an organized cohort.
arXiv Detail & Related papers (2024-08-06T22:09:50Z)
Wireless Crowd Detection for Smart Overtourism Mitigation [50.031356998422815]
This chapter describes a low-cost approach to monitoring overtourism based on mobile devices' wireless activity. The crowding sensors count the number of surrounding mobile devices, by detecting trace elements of wireless technologies. They run detection programs for several technologies, and fingerprinting analysis results are only stored locally in an anonymized database.
arXiv Detail & Related papers (2024-02-14T13:20:24Z)
YOLORe-IDNet: An Efficient Multi-Camera System for Person-Tracking [2.5761958263376745]
We propose a person-tracking system that combines correlation filters and Intersection Over Union (IOU) constraints for robust tracking. The proposed system quickly identifies and tracks suspect in real-time across multiple cameras. It is computationally efficient and achieves a high F1-Score of 79% and an IOU of 59% comparable to existing state-of-the-art algorithms.
arXiv Detail & Related papers (2023-09-23T14:11:13Z)
Enhancing Multi-Camera People Tracking with Anchor-Guided Clustering and Spatio-Temporal Consistency ID Re-Assignment [22.531044994763487]
We propose a novel multi-camera multiple people tracking method that uses anchor clustering-guided for cross-camera reassigning. Our approach aims to improve accuracy of tracking by identifying key features that are unique to every individual. The method has demonstrated robustness and effectiveness in handling both synthetic and real-world data.
arXiv Detail & Related papers (2023-04-19T07:38:15Z)
Scalable and Real-time Multi-Camera Vehicle Detection, Re-Identification, and Tracking [58.95210121654722]
We propose a real-time city-scale multi-camera vehicle tracking system that handles real-world, low-resolution CCTV instead of idealized and curated video streams. Our method is ranked among the top five performers on the public leaderboard.
arXiv Detail & Related papers (2022-04-15T12:47:01Z)
Benchmarking high-fidelity pedestrian tracking systems for research, real-time monitoring and crowd control [55.41644538483948]
High-fidelity pedestrian tracking in real-life conditions has been an important tool in fundamental crowd dynamics research. As this technology advances, it is becoming increasingly useful also in society. To successfully employ pedestrian tracking techniques in research and technology, it is crucial to validate and benchmark them for accuracy. We present and discuss a benchmark suite, towards an open standard in the community, for privacy-respectful pedestrian tracking techniques.
arXiv Detail & Related papers (2021-08-26T11:45:26Z)
Self-supervised Human Detection and Segmentation via Multi-view Consensus [116.92405645348185]
We propose a multi-camera framework in which geometric constraints are embedded in the form of multi-view consistency during training. We show that our approach outperforms state-of-the-art self-supervised person detection and segmentation techniques on images that visually depart from those of standard benchmarks.
arXiv Detail & Related papers (2020-12-09T15:47:21Z)
Artificial Intelligence Enabled Traffic Monitoring System [3.085453921856008]
This article presents a novel approach to automatically monitor real time traffic footage using deep convolutional neural networks. The proposed system deploys several state-of-the-art deep learning algorithms to automate different traffic monitoring needs.
arXiv Detail & Related papers (2020-10-02T22:28:02Z)
Tracking Passengers and Baggage Items using Multi-camera Systems at Security Checkpoints [0.7424262881242935]
We introduce a novel tracking-by-detection framework to track multiple objects in overhead camera videos for airport checkpoint security scenarios. Our approach improves object detection by employing a test-time data augmentation procedure. An evaluation of detection, tracking, and association performances on videos obtained from multiple overhead cameras in a realistic airport checkpoint environment demonstrates the effectiveness of the proposed approach.
arXiv Detail & Related papers (2020-07-15T18:09:31Z)
RetinaTrack: Online Single Stage Joint Detection and Tracking [22.351109024452462]
We focus on the tracking-by-detection paradigm for autonomous driving where both tasks are mission critical. We propose a conceptually simple and efficient joint model of detection and tracking, called RetinaTrack, which modifies the popular single stage RetinaNet approach.
arXiv Detail & Related papers (2020-03-30T23:46:29Z)

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