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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | People Counting System Based on Head Detection R-CNN from Images and Videos |
| Authors Name: | MUSKU ARUN KUMAR , MULUKUNTLA RATHAN KUMAR , MERUGU HRUTHIK , MOTHE TEJA , DR. K.P.KALIYAMURTHIE |
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IJNRD_217264
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| Published Paper Id: | IJNRDTH00125 |
| Published In: | Volume 9 Issue 4, April-2024 |
| DOI: | |
| Abstract: | 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. |
| Keywords: | Detection, RCNN, Count |
| Cite Article: | "People Counting System Based on Head Detection R-CNN from Images and Videos", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 4, page no.268-329, April-2024, Available :http://www.ijnrd.org/papers/IJNRDTH00125.pdf |
| Downloads: | 00080 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, 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 |
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Published Paper ID:IJNRDTH00125 Registration ID: 217264 Published In: Volume 9 Issue 4, April-2024 DOI (Digital Object Identifier): Page No: 268-329 Country: CHENNAI, TAMIL NADU, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRDTH00125 Published Paper PDF: https://www.ijnrd.org/papers/IJNRDTH00125 |
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