arXiv subjects

Md. Omar Faruqe

Publications and source records attributed to Md. Omar Faruqe.

2 recordsLinked to original sources

arXiv2026

Beyond Ray-Casting: Evaluating Controller, Free-Hand, and Virtual-Touch Modalities for Immersive Text Entry

Efficient text entry remains a primary bottleneck preventing Virtual Reality (VR) from evolving into a viable productivity platform. To address this, we conducted an empirical comparison of six physical input systems across three interaction styles Controller Driven, Free Hand, and Virtual Touch evaluating both discrete tap typing and continuous gesture typing (swiping), alongside a speech to text (Voice) condition as a non physical reference modality. Results from 21 participants show that the Controller Driven Tap Gesture Combo (CD TGC) delivers the best productivity performance, achieving speeds 2.25 times higher than the slowest system and 30% faster than the current industry standard, while reducing error rates by up to 68%. A clear trade off emerged between performance and perceived usability: although controller based gesture input led on speed and accuracy, participants rated Virtual Touch Tap Typing highest in subjective experience, scoring 80% higher on the System Usability Scale (SUS) than the lowest rated alternative. We further observe that Free Hand interaction remains limited by tracking stability and physical fatigue, whereas Voice input introduces practical constraints related to privacy, editing control, and immersive engagement. Together, these findings characterize the tension between throughput and natural interaction in immersive text entry and provide data driven guidance for future VR interface design.

cs.HC↗

arXiv2026

Automated Wicket-Taking Delivery Segmentation and Trajectory-Based Dismissal-Zone Analysis in Cricket Videos Using OCR-Guided YOLOv8

Cricket generates a rich stream of visual and contextual information, yet much of its tactical analysis still depends on slow and subjective manual review. Motivated by the need for a more efficient and data-driven alternative, this paper presents an automated approach for cricket video analysis that identifies wicket-taking deliveries, detects the pitch and ball, and models ball trajectories for post-match assessment. The proposed system combines optical character recognition (OCR) with image preprocessing techniques, including grayscale conversion, power transformation, and morphological operations, to robustly extract scorecard information and detect wicket events from broadcast videos. For visual understanding, YOLOv8 is employed for both pitch and ball detection. The pitch detection model achieved 99.5% mAP50 with a precision of 0.999, while the transfer learning-based ball detection model attained 99.18% mAP50 with 0.968 precision and 0.978 recall. Based on these detections, the system further models ball trajectories to reveal regions associated with wicket-taking deliveries, offering analytical cues for trajectory-based dismissal-zone interpretation and potential batting vulnerability assessment. Experimental results on multiple cricket match videos demonstrate the effectiveness of the proposed approach and highlight its potential for supporting coaching, tactical evaluation, and data-driven decision-making in cricket.

cs.CV↗