IT Support Projects in Kenitra
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EL MEHDI HICHAM
This TactiVision demo analyses Harry Kane’s penalty against Paris Saint-Germain using an automatic goal-confirmation pipeline. The system combines: goal keypoint estimation confidence-aware filtering goal-line geometry YOLO26s goal-mask segmentation ball detection and temporal tracking multi-frame goal confirmation Unlike standard object detection, the objective is not simply to detect the goal or the ball. The system must determine whether the ball crosses the reconstructed line inside the valid goal-mouth region. Kane scored the penalty in the 17th minute of Bayern’s Champions League semi-final first leg against PSG. #HarryKane (https://www.youtube.com/hashtag/harrykane) #BayernMunich (https://www.youtube.com/hashtag/bayernmunich) #PSG (https://www.youtube.com/hashtag/psg) #ChampionsLeague (https://www.youtube.com/hashtag/championsleague) #FootballAI (https://www.youtube.com/hashtag/footballai) #TactiVision (https://www.youtube.com/hashtag/tactivision)
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ISMAIL HAJJY
Smart Home Automation System with Raspberry Pi
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EL MEHDI HICHAM
Detecting a goal is easy. Knowing whether the ball actually crossed the line is a geometry problem. In this #TactiVision R&D module, I combine: goal keypoint detection to anchor the goal geometry goal segmentation to estimate the goal area confidence filtering geometric projection polygon-based validation temporal stabilization The main challenge is robustness: a single weak keypoint near the post can shift the projected goal line, so the system must decide which geometric information can be trusted before confirming a goal. This module is part of my broader football Computer Vision pipeline for detection, tracking, pitch mapping and tactical analysis
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EL MEHDI HICHAM
7 AI Models Analyzing Football in Real-Time — This Is Footbal
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