Trends in Robotics and Computer Integrated Manufacturing

Dixit, Anil Kumar; Kumar, T. Vijaya; Joshi, Abhishek; Bedi, Harpreet Singh; Chakravarthi, M. Kalyan; Singh, Devesh Pratap

doi:10.1109/ic3i56241.2022.10072745

Cited by 2 publications

(1 citation statement)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To tackle the problems faced by sliding window detection, we intend to use a Deep Learning based algorithm, You Only Look Once (YOLO) [14]. After referring to several research papers and performing an analysis on ratio between accuracy and the computation involved, we have concluded that YOLO is the best algorithm for this use case [15].…”

Section: Object Detection Using Yolomentioning

confidence: 99%

Traffic Surveillance Using Computer Vision and Deep Learning Methods

Bedi,

Malik,

Singh

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

Self Cite

View full text Add to dashboard Cite

From the early 21st century to the present date, Traffic surveillance has been one of the most challenging problems and over the years many solutions were proposed for tackling this problem. However, flaws are can still be noticed in the currently existing systems for performing this task and some of them will be further addressed in this work. In our project Traffic Surveillance using Computer Vision and Deep Learning, we propose a smart vision-based system which accurately provides live traffic statistics like the speed of the traffic and the count of Vehicles passed on a road in between a time interval. The system takes a video feed as an input, which could be live footage provided by a CCTV camera at a traffic junction, and on querying starting and ending timestamps, it outputs the count of the vehicles and speed of the traffic in between the queried timestamps. We divide this problem into two major sub-problems, firstly detecting a vehicle, followed by tracking the detected vehicle. Although there exist many algorithms for detecting objects as well as for tracking objects, one of the major problems is integrating both. As ambiguities like detecting an already detected vehicle, many occur while tracking the vehicles. In our work, we emphasize handling such ambiguities using Deep Learning and we also provide a comparative performance analysis of our system with the already existing ones.

show abstract

Section: Object Detection Using Yolomentioning

confidence: 99%

Traffic Surveillance Using Computer Vision and Deep Learning Methods

Bedi,

Malik,

Singh

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

Precision and Performance

Dileepkumar,

Gouda,

Kumasi

2024

Advances in Human Services and Public Health

View full text Add to dashboard Cite

The confluence of precision medicine and advanced healthcare technologies is revolutionizing sports medicine by refining diagnostics, optimizing therapeutic interventions, and elevating performance metrics. This chapter delves into the transformative role of smart healthcare systems, including wearable biosensors, machine learning algorithms, and data-driven analytics, which collectively enable a nuanced understanding of athletic health. By enhancing early injury detection and fostering customized rehabilitation strategies, these technologies epitomize a paradigmatic shift toward individualized, anticipatory healthcare in sports. Our analysis elucidates both the mechanistic functionalities and the physiological implications of these innovations, which promise to redefine the contours of athletic resilience and human potential.

show abstract

Trends in Robotics and Computer Integrated Manufacturing

Cited by 2 publications

References 18 publications

Traffic Surveillance Using Computer Vision and Deep Learning Methods

Traffic Surveillance Using Computer Vision and Deep Learning Methods

Precision and Performance

Contact Info

Product

Resources

About