A Systematic Approach for Exploring Underground Environment Using LiDAR-Based System

Agricultural projects in different parts of the world depend on underground water wells.Recently,there have been many unfortunate incidents inwhich children have died in abandoned undergroundwells.Providing topographical information for these wells is a prerequisite to protecting people from the dangers of falling into them,especially since most of these wells become buried over time.Many solutions have been developed recently,most with the aimof exploring these well areas.However,these systems suffer fromseveral limitations,including high complexity,large size,or inefficiency.This paper focuses on the development of a smart exploration unit that is able to investigate underground well areas,build a 3D map,search for persons and animals,and determine the levels of oxygen and other gases.The exploration unit has been implemented and validated through several experiments using various experiment testbeds.The results proved the efficiency of the developed exploration unit,in terms of 3D modeling,searching,communication,and measuring the level of oxygen.The average accuracy of the 3D modeling function is approximately 95.5%.A benchmark has been presented for comparing our results with related works,and the comparison has proven the contributions and novelty of the proposed system’s results.

Automatic Team Assignment and Jersey Number Recognition in Football Videos

Football is one of the most-watched sports,but analyzing players’per-formance is currently difficult and labor intensive.Performance analysis is done manually,which means that someone must watch video recordings and then log each player’s performance.This includes the number of passes and shots taken by each player,the location of the action,and whether or not the play had a successful outcome.Due to the time-consuming nature of manual analyses,interest in automatic analysis tools is high despite the many interdependent phases involved,such as pitch segmentation,player and ball detection,assigning players to their teams,identifying individual players,activity recognition,etc.This paper proposes a system for developing an automatic video analysis tool for sports.The proposed system is the first to integrate multiple phases,such as segmenting the field,detecting the players and the ball,assigning players to their teams,and iden-tifying players’jersey numbers.In team assignment,this research employed unsu-pervised learning based on convolutional autoencoders(CAEs)to learn discriminative latent representations and minimize the latent embedding distance between the players on the same team while simultaneously maximizing the dis-tance between those on opposing teams.This paper also created a highly accurate approach for the real-time detection of the ball.Furthermore,it also addressed the lack of jersey number datasets by creating a new dataset with more than 6,500 images for numbers ranging from 0 to 99.Since achieving a high perfor-mance in deep learning requires a large training set,and the collected dataset was not enough,this research utilized transfer learning(TL)to first pretrain the jersey number detection model on another large dataset and then fine-tune it on the target dataset to increase the accuracy.To test the proposed system,this paper presents a comprehensive evaluation of its individual stages as well as of the sys-tem as a whole.