People Counting System Based on Head Detection R-CNN from Images and Videos

Registration ID: IJNRD_217264

Published ID: IJNRDTH00125

: Click Here to Get

About Hard Copy and Transparent Peer Review Report

MUSKU ARUN KUMAR, MULUKUNTLA RATHAN KUMAR, MERUGU HRUTHIK, MOTHE TEJA, DR. K.P.KALIYAMURTHIE

Detection, RCNN, Count

In contemporary society, the need for accurate people counting systems has become increasingly vital across various domains such as retail analytics, crowd management, and security surveillance. Traditional methods for people counting often rely on manual observation or simple sensor-based approaches, which are prone to inaccuracies, especially in crowded environments with occlusions and complex dynamics. To address these challenges, advanced computer vision techniques have been developed, with deep learning-based methods showing remarkable success in recent years. This abstraction provides an in-depth overview of a people counting system based on head detection utilizing Faster R-CNN from both images and videos. The proposed system leverages Faster R-CNN, a state-of-the-art deep learning framework, to detect heads in input images and video frames. Faster R-CNN is a two-stage object detection architecture that consists of a Region Proposal Network (RPN) for generating region proposals and a Region-Based Convolutional Neural Network (R-CNN) for refining these proposals into precise object detections. By employing Faster R-CNN, the system achieves high accuracy and robustness in head detection, enabling reliable people counting in diverse scenarios. The system architecture encompasses several key components, including data acquisition, preprocessing, head detection using Faster R-CNN, post-processing, and counting. Initially, data is acquired from surveillance cameras or other sources, providing input images and video streams. Preprocessing techniques such as image stabilization, noise reduction, and contrast enhancement are applied to improve the quality of the input data and facilitate effective head detection. Faster R-CNN is then utilized to detect heads within the preprocessed images and video frames. The RPN generates candidate regions likely to contain heads, which are subsequently refined by the R-CNN to produce accurate head detections. The network is trained on annotated datasets to learn discriminative features of heads, enabling it to generalize well to unseen data and various environmental conditions. Through this process, Faster R-CNN effectively identifies heads in both images and video frames, even in challenging scenarios with occlusions and varying lighting conditions. Following head detection, post-processing techniques are applied to refine the detected regions and eliminate duplicate detections. Non-maximum suppression (NMS) and bounding box filtering are commonly employed to suppress redundant detections and ensure that each head is counted only once. These post-processing steps help improve the accuracy and reliability of the people counting system, particularly in crowded scenes where multiple heads may overlap or occlude each other. Subsequently, the system performs counting by associating unique identifiers with detected heads and tracking their movement across consecutive frames in videos or images. Various tracking algorithms such as Kalman filtering or centroid-based tracking may be employed to maintain continuity in counting, even in scenarios involving occlusions or temporary disappearances. By tracking individuals over time, the system accurately determines the total number of people present in the monitored area. Moreover, the proposed system incorporates additional features for performance evaluation and user interaction. Metrics such as accuracy, precision, recall, and F1 score are utilized to assess the effectiveness of head detection and counting. Visualization tools enable real-time monitoring of the counting process, allowing operators to analyze crowd dynamics and make informed decisions. Additionally, the system supports interactive functionalities such as manual validation of detections and adjustment of counting parameters, enhancing user control and flexibility. Furthermore, the system architecture is designed to be scalable and adaptable to diverse environments. It supports parallel processing and distributed computing to handle large-scale deployments across multiple surveillance zones. Additionally, model retraining mechanisms enable continuous improvement and adaptation to evolving scenarios and demographics. In conclusion, the abstraction presents a comprehensive framework for a people counting system based on head detection using Faster R-CNN from both images and videos. By harnessing the power of deep learning and computer vision, the system offers accurate, efficient, and scalable solutions for crowd management and surveillance applications. Future research directions may focus on optimizing model performance, enhancing robustness to challenging scenarios, and integrating additional contextual information for advanced analytics and decision support.

Published Paper   E-Certificate

MUSKU ARUN KUMAR, MULUKUNTLA RATHAN KUMAR, MERUGU HRUTHIK, MOTHE TEJA, & DR. K.P.KALIYAMURTHIE (April-2024). People Counting System Based on Head Detection R-CNN from Images and Videos. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(4), 268-329. https://ijnrd.org/papers/IJNRDTH00125.pdf

Volume 9 Issue 4, April-2024

Pages : 268-329

Paper Reg. ID: IJNRD_217264

Published Paper Id: IJNRDTH00125

Research Area: Computer Science & Technology 

Author Type: Indian Author

Country: CHENNAI, TAMIL NADU, India

Published Paper PDF: https://ijnrd.org/papers/IJNRDTH00125.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRDTH00125

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

UGC CARE JOURNAL PUBLICATION | ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International UGC CARE JOURNAL PUBLICATION, Low Cost, Scholarly Open Access, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJNRD (IJ Publication) Janvi Wave | IJNRD.ORG | IJNRD.COM | IJPUB.ORG

© 2026 - Authors hold the copyright of this article. This work is licensed under a Creative Commons Attribution 4.0 International License. and The Open Definition. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0). 🛡️ Disclaimer: The content, data, and findings in this article are based on the authors’ research and have been peer-reviewed for academic purposes only. Readers are advised to verify all information before practical or commercial use. The journal and its editorial board are not liable for any errors, losses, or consequences arising from its use.

Call For Paper - Volume 11 | Issue 1 | January 2026

IJNRD is a Scholarly Open Access, Peer-Reviewed, Refereed, and UGC CARE Journal Publication with a High Impact Factor of 8.76 (calculated by Google Scholar & Semantic Scholar | AI-Powered Research Tool). It is a Multidisciplinary, Monthly, Low-Cost, and Transparent Peer Review Journal Publication that adheres to the New UGC CARE Transparent Peer-Reviewed Journal Policy and aligns with Scopus Journal Publication standards to ensure the highest level of research quality and credibility.

IJNRD offers comprehensive Journal Publication Services including indexing in all major databases and metadata repositories, Digital Object Identifier (Crossref DOI) assignment for each published article with additional fees, citation generation tools, and full Open Access visibility to enhance global research reach and citation impact.

The INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (IJNRD) aims to advance applied, theoretical, and experimental research across diverse academic and professional fields. The journal promotes global knowledge exchange among researchers, developers, academicians, engineers, and practitioners, serving as a trusted platform for innovative, peer-reviewed journal publication and scientific collaboration.

Indexing Coverage: Google Scholar, SSRN, ResearcherID-Publons, Semantic Scholar (AI-Powered Research Tool), Microsoft Academic, Academia.edu, arXiv.org, ResearchGate, CiteSeerX, ResearcherID (Thomson Reuters), Mendeley, DocStoc, ISSUU, Scribd, and many other recognized academic repositories.

Transparent Peer Review Journal Publication: IJNRD operates a strict double-blind peer review system managed by 3000+ expert reviewers, ensuring ethical, unbiased, and high-quality review for every research paper.

For Indian Authors : Get a transparent peer review report from Scholar9.com for just ₹1000. View Sample Report

For Foreign Authors : A detailed peer review report is available through Scholar9.com for $20 USD. View Sample Report

Transparent Peer Review Journal Publication

⭐ Transparent Peer Review | 🕵️‍♂️ Double-Blind | 👨‍🏫 3000+ Expert Reviewers | 🇮🇳 Report for India Author ₹1000 | 🌐 Report for Foreign Author $20 | 📄 Sample Reports on Scholar9.com | 🌍 High Credibility | ⚖️ Ethical & Unbiased Evaluation

How to submit the paper?

By Our website

Click Here to Submit Paper Online

By Mail

Send your paper to editor@ijnrd.org

Recently, the UGC discontinued the UGC-CARE Journal List and introduced new parameters that allow publication in Transparent Peer-Reviewed (Refereed) Journals. IJNRD is Transparent Peer Review Journal Valid As per New UGC Notification.

You can now publish your research paper in IJNRD.ORG. IJNRD is a Transparent Peer-Reviewed Open Access (Refereed Journal), UGC and UGC CARE Approved, Crossref DOI, Multidisciplinary, Impact Factor calculate by Google Scholar. As an International, open-access, and online journal, Publishing with us ensures wider reach, academic credibility, and enhanced recognition for your work.

For more details, refer to the official notice: UGC Public Notice

⭐ Low Cost ₹1570 | 📚 UGC CARE Approved | 🔍 Peer-Reviewed | 🌐 Open Access | 🔗 Crossref DOI & Global Indexing | 📊 Google Scholar Impact Factor | 🧪 Multidisciplinary

Submit Paper Online  Call for Paper  About IJNRD UGC CARE Approval

Important Dates for Current issue

Paper Submission Open For: January 2026

Current Issue: Volume 11 | Issue 1 | January 2026

Impact Factor: 8.76

Last Date for Paper Submission: Till 31-Jan-2026

Notification of Review Result: Transparent peer review process - your paper is evaluated by experts, and you receive acceptance or rejection updates via email and SMS.

Publication of Paper: Once all documents are submitted, your paper is published without delay, and you can instantly download your certificate and confirmation letter online.

Frequency: Monthly (12 issue Annually).

Journal Type: IJNRD is an international open-access journal offering Low Cost Journal Publication, transparent Peer Review Journal Publication, Crossref DOI, and multidisciplinary research visibility under UGC CARE Approved Journal Publication.

Subject Category: Research Area

Approval, Licenses and Indexing: More Details

Call For Paper - Volume 11 | Issue 1 | January 2026

IJNRD.org offers low-cost journal publication starting at ₹1570 with UGC CARE Approved, refereed, peer-reviewed, open-access publishing. This multidisciplinary monthly journal, available in both online and print formats, features a strong Google Scholar-based impact factor of 8.76, Transparent Peer Review, CrossRef DOI, global indexing, fast publication, and complete metadata for maximum research visibility and citation impact across multidisciplinary domains.

Volume 11 | Issue 1 | January 2026 | IJNRD Transparent Peer Review Certificate | Submit Paper Online till 31-Jan-2026

⭐ UGC CARE Approved Refereed Journal | 🔍 Transparent Peer Review | 🌐 Open Access Publishing | 💰 Low-Cost ₹1570 | 🔗 CrossRef DOI & Global Indexing | 📊 Google Scholar Impact Factor 8.76 | 🧪 Multidisciplinary | Online & Print

Submit Paper Online