An Automated Hierarchical Framework for Player Recognition in Sports Image

Atrish, Abhay; Singh, Navjot; Kumar, Krishan; Kumar, Vinod

doi:10.1145/3177404.3177432

Cited by 13 publications

(1 citation statement)

References 16 publications

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“…Author [39] proposes a method for automatically extracting and recognizing a player's jersey number using optical character recognition (OCR). A hierarchical computation framework is presented to identify the player in an image that utilizes low-and high-level vision features.…”

Section: Limitationsmentioning

confidence: 99%

An improved deep learning-based optimal object detection system from images

Yadav,

Jindal,

Rani

et al. 2023

Multimed Tools Appl

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Computer vision technology for detecting objects in a complex environment often includes other key technologies, including pattern recognition, artificial intelligence, and digital image processing. It has been shown that Fast Convolutional Neural Networks (CNNs) with You Only Look Once (YOLO) is optimal for differentiating similar objects, constant motion, and low image quality. The proposed study aims to resolve these issues by implementing three different object detection algorithms—You Only Look Once (YOLO), Single Stage Detector (SSD), and Faster Region-Based Convolutional Neural Networks (R-CNN). This paper compares three different deep-learning object detection methods to find the best possible combination of feature and accuracy. The R-CNN object detection techniques are performed better than single-stage detectors like Yolo (You Only Look Once) and Single Shot Detector (SSD) in term of accuracy, recall, precision and loss.

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Section: Limitationsmentioning

confidence: 99%

An improved deep learning-based optimal object detection system from images

Yadav,

Jindal,

Rani

et al. 2023

Multimed Tools Appl

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Enhanced Homography-Based Sports Image Components Analysis System

Atrish

Singh

Kumar

2018

Advances in Intelligent Systems and Computing

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A novel multi-image cryptosystem based on weighted plain images and using combined chaotic maps

et al. 2021

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This paper introduces a new multi-image cryptosystem based on modified Henon map and nonlinear combination of chaotic seed maps. Based on the degree of correlation between the adjacent pixels of the plain image, a unique weight is assigned to the plain image. First, the coordinates of plain images are disrupted by modified Henon map as confusion phase. In the first step of diffusion phase, the pixels content of images are changed separately by XOR operation between confused images and matrices with suitable nonlinear combination of seed maps sequences. These combination of seed maps are selected depending on the weight of plain images as well as bifurcation properties of mentioned chaotic maps. After concatenating the matrices obtained from the first step of diffusion phase, the bitwise XOR operation is applied between newly developed matrix and the other produced matrix from the chaotic sequences of the Logistic-Tent-Sine hybrid system, as second step of diffusion phase. The encrypted image is obtained after applying shift and exchange operations. The results of the implementation using graphs and histograms show that the proposed scheme, compared to some existing methods, can effectively resist common attacks and can be used as a secure method for encrypting digital images.

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An Automated Hierarchical Framework for Player Recognition in Sports Image

Cited by 13 publications

References 16 publications

An improved deep learning-based optimal object detection system from images

An improved deep learning-based optimal object detection system from images

Enhanced Homography-Based Sports Image Components Analysis System

A novel multi-image cryptosystem based on weighted plain images and using combined chaotic maps

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