Deep Learning for People Counting in Videos by Age and Gender

Inácio, Andrei de Souza; Ramos, Rafael Hora; Lopes, Heitor Silvério

doi:10.21528/cbic2021-53

Cited by 2 publications

(4 citation statements)

References 17 publications

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“…This was another intelligence-based system showcasing object detection means and DL technique. For counting persons in films, In´acio et al (2021) [15] [16] enhanced the functionality of the selective refinement network (SRN) algorithm. The research project combined existing methods, such as a decoupled classification module, to create an SRN face detection.…”

Section: Related Studiesmentioning

confidence: 99%

Performance investigation of two-stage detection techniques using traffic light detection dataset

Ajagbe

Adegun

Olanrewaju

et al. 2023

IJ-AI

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<span>Using a camera to monitor an object or a group of objects over time is the process of object detection. It can be used for a variety of things, including security and surveillance, video communication, traffic light detection (TLD), object detection from compressed video in public places. In recent times, object tracking has become a popular topic in computer science particularly, the data science community, thanks to the usage of deep learning (DL) in artificial intelligence (AI). DL which convolutional neural network (CNN) as one of its techniques usually used two-stage detection methods in TLD. Despite all successes recorded in TLD through the use of two-stage detection methods, there is no study that has analyzed these methods in experimental research, studying the strength and witnesses by the researchers. Based on the needs this study analyses the applications of DL techniques in TLD. We implemented object detection for TLD using 5 two-stage detection methods with the traffic light dataset using a Jupyter notebook and the sklearn libraries. We present the achievements of two-stage detection methods in TLD, going by standard performance metrics used, FASTER-CNN was the best in detection accuracy, F1-score, precision and recall with 0.89, 0.93, 0.83 and 0.90 respectively.</span>

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Section: Related Studiesmentioning

confidence: 99%

Performance investigation of two-stage detection techniques using traffic light detection dataset

Ajagbe

Adegun

Olanrewaju

et al. 2023

IJ-AI

View full text Add to dashboard Cite

show abstract

“…Ornek, et al, [4] Counting people in videos is a crucial task and is applicable in surveillance, businesses, health care services etc. In´acio et al, [5] proposed a deep learning approach system for counting people in videos paying special attention to gender and age information. The developed system made use of specialized Deep Neural Networks (DNN) to extract gender and age information from faces detected in videos.…”

Section: Overview Of Deep Learning Algorithms Application In Object D...mentioning

confidence: 99%

“…Kumar, et al, [3] Hybrid deep CNN classifier with IoTbased device data access Efficient real-time face mask detection in the transportation system. 2 Ornek, et al, [4] ResNet-18 deep neural network model Improved classification of face images into masked and non-masked 3 In´acio, et al, [5] DNN-based on Efficient Net technique Efficient taction of gender and age in a video dataset 4 Torres et al, [6] ODIN model An open-source object detection and instance segmentation error diagnosis system developed 5 Chi, et al, [7] Selective Refinement Network (SRN) with the introduction of a Receptive Field Enhancement (RFE) block Minimization of false positives and improvement of location accuracy simultaneously. It also aids in capturing faces in extreme positions.…”

Section: Overview Of Deep Learning Algorithms Application In Object D...mentioning

confidence: 99%

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Applications and Challenges of Deep Learning in Object Detection: A Survey

2022

JCBSC

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Deep Learning (DL) of Artificial intelligence (AI) is a hot topic in the data science world. It's become crucial as many public and commercial businesses amass huge amounts of domain-specific data, which can provide useful information on issues like fraud detection, national intelligence, cyber security, medical informatics and marketing. Microsoft, Google, Twitter and Amazon Web Services for example, are evaluating very huge amounts of data for business analysis and decisions making, which has an impact on current and future technologies. Through the hierarchical learning process, DL algorithms extract high-level, complex abstractions as data representations. Scene interpretation, video surveillance, robots, and self-driving systems are just a few of the many applications that have prompted an extensive study in the field of computer vision in the last decade. Visual recognition systems, which include picture categorization, localization, and detection, are at the heart of all of these applications and have gathered a lot of research attention. These visual identification algorithms have achieved extraordinary performance, thanks to considerable advancements in neural networks, in particular deep learning. Object tracking and detection are one of the areas where computer vision has had a lot of success. The aim of this paper is to analyze the applications and challenges of DL algorithms for object detection in the last ten (10) years. The state-of-the-art object detection methods, including video tracking, are Applications … Yetunde Josephine OGUNS et al.

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Deep Learning for People Counting in Videos by Age and Gender

Cited by 2 publications

References 17 publications

Performance investigation of two-stage detection techniques using traffic light detection dataset

Performance investigation of two-stage detection techniques using traffic light detection dataset

Applications and Challenges of Deep Learning in Object Detection: A Survey

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