Face analysis in video: face detection and tracking with pose estimation

Mliki, Hazar; Hammami, Mohamed

doi:10.1504/ijbm.2018.10012738

Cited by 1 publication

(1 citation statement)

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“…Yang [34] proposed the moving video image target tracking and recognition based on CNN feature selection in which feature centers are generated as per the distance matrix between feature images and feature dimensions are compressed. In the literature, there are many studies on face detection and tracking algorithms, Mliki and Hammami [35], had introduced a full automatic approach to achieve face detection and tracking with estimation in video sequences. In their work, they propose a combination between detection and tracking to overcome the various challenging problems that might occur while detecting or tracking faces.…”

Section: Tracking Algorithmmentioning

confidence: 99%

Object detection on robosoccer environment using convolution neural network

Steffi¹,

Mehta²,

Venkatesh

2022

IJEECS

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Robots with autonomous capabilities depend on vision capabilities to detect and interact with objects and their environment. In the field of robotic research, one of the focus areas is the robosoccer platform that is being used to implement and test new ideas and findings on computer vision and decision making. In this article, an efficient real-time object detection algorithm is employed in a robosoccer simulation environment by deploying a convolution neural network and Kalman filter based tracking algorithms. This study's objective is to classify nao, ball, and the goalpost as well as to validate nao and ball tracking without human intervention from initial frame to last frame. In comparison with the existing methods, the proposed method is robust and fast in identifying three classes namely nao, ball, and goalpost with a speed of 1.67 FPS and a mAP of 95.18%. By implementing this approach, soccer playing robots can make appropriate decisions during game play.

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Section: Tracking Algorithmmentioning

confidence: 99%