An Innovative Machine Learning Approach for Object Detection and Recognition

Sarkale, Aishwarya; Shah, Kaiwant; Chaudhary, Anandji; Nagarhalli, Tatwadarshi P.

doi:10.1109/icicct.2018.8473221

Cited by 6 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Mobile Nets are built using depth-wise separable convolutions as its building blocks. After the standard convolution, the input feature map is divided up into a number of different feature maps [2]. Convolution in the context of Depth wise Separable Convolution [2].…”

Section: E Mobile Nets Algorithmmentioning

confidence: 99%

“…After the standard convolution, the input feature map is divided up into a number of different feature maps [2]. Convolution in the context of Depth wise Separable Convolution [2]. When compared to the work done by the network using normal convolutions having the same depth, this model's implementation of depth-wise separable convolutions results in a sizeable reduction in the total number of parameters.…”

Section: E Mobile Nets Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

A Study of YOLO-Based Object Detection for Visually Impaired Individuals

Chandrakar

2024

IJRASET

View full text Add to dashboard Cite

An essential area of investigation involves recognizing and tracking objects, given the frequent changes in object motion, scene dimensions, occlusions, appearance, ego-motion, and lighting variations. Effective object tracking heavily relies on feature selection due to its critical role. This process is integral to numerous real-time applications like vehicle detection and video surveillance. To address detection challenges, tracking methods need to be adept at handling object movement and appearance changes. Among these methods, tracking algorithms play a pivotal role in smoothing video sequences and receive significant attention. However, only a few approaches leverage previously gathered data on object attributes such as shape and color. This study delves into a tracking algorithm that encompasses all these object properties. Its aim is to investigate and assess past methodologies for tracking and detecting moving objects across various stages of video sequences. Additionally, it aims to identify existing challenges and propose innovative strategies to enhance object tracking throughout video frames

show abstract

Section: E Mobile Nets Algorithmmentioning

confidence: 99%

Section: E Mobile Nets Algorithmmentioning

confidence: 99%