This　paper　aims　to　segment　eye　movements　in　videos　captured　in　outdoor　environments　and　extract　the　parameters　of　the　ellipse　fitted　to　the　pupil,　thereby　improving　the　accuracy　of　gaze　tracking　in　natural　settings.　Current　eye-tracking　methods　face　challenges　in　complex　outdoor　environments　due　to　lighting　variations,　head　movements,　eyelid　occlusions,　and　changes　in　camera　angles,　leading　to　reduced　accuracy　in　eye　segmentation　and　pupil　parameter　extraction.　To　address　these　issues,　I　propose　a　framework,　EyeCRNet,　designed　for　pupil　fitting　in　outdoor　eye　movement　analysis.　This　framework　includes　two　main　components:　eye　segmentation　and　pupil　ellipse　fitting,　to　enhance　the　robustness　of　pupil　segmentation　in　outdoor　conditions.　First,　the　EyeCRNet　model　is　trained　on　the　OpenEDS2019　dataset　to　ensure　accurate　eye　segmentation.　Then,　videos　from　the　LPW　dataset　are　segmented　into　500　frames,　and　manual　segmentation　of　the　pupil,　iris,　and　sclera　is　performed　on　these　eye　movement　video　frames.　EyeCRNet　utilizes　the　video　dataset　and　employs　an　attention　mechanism　to　strengthen　the　association　between　the　pupil　and　corneal　reflection　points　(CR).　Through　multi-task　learning,　the　model　effectively　improves　the　detection　of　the　pupil　and　corneal　reflection　points　(CR)　in　natural　environments,　enhancing　prediction　stability　and　robustness.　Additionally,　it　accurately　calculates　the　geometric　parameters　of　the　fitted　pupil　ellipse,　including　center　position,　major　and　minor　axes,　and　rotation　angle.　Experimental　results　demonstrate　that,　the　EyeCRNet　network　effectively　adapts　to　lighting　and　dynamic　eye　changes.　©　The　Institution　of　Engineering　&　Technology　2025.