Night-time perimeter monitoring in low illumination remains challenging because cluttered terrain, partial occlusion, and thermal crossover conditions simultaneously distort object boundaries and visual cues. Visible spectrum sensing loses discriminative texture at night, while infrared sensing preserves thermal salience but lacks structural context. Conventional pipelines that rely on single-modality detection or simple blending commonly produce unstable recall or avoidable false alarms when the background changes. The present paper presents an EfficientDet Fusion Intrusion Detector (EFID) which combines registered infrared and visible frames using an attention-guided weighting module and a spatially adaptive activity-weighted pixel fusion step. The feature-level attention weights estimate the reliability of each modality and guide the activity-weighted pixel fusion stage. The fused RGB image is resized from 1024 × 768 to 896 × 896 before being processed by EfficientDet-D3. The resultant fusion retains visible structural edges, while infrared target evidence is simultaneously enhanced. The resulting three-channel representation is processed by an EfficientDet D3 detector augmented with a bidirectional feature pyramid network (BiFPN) for multi-scale localisation under night conditions and clutter. Experiments use the public Multi-scenario Multi-modality Fusion and Detection (M³FD) benchmark containing 4,200 aligned infrared and visible pairs at 1024×768 resolution, annotated with 33,603 bounding boxes across six classes. The proposed EFID achieves 91.7% mAP@0.5 and 67.3% mAP@0.5:0.95, with 93.4% precision and 89.8% recall at 34.2 FPS on an NVIDIA RTX 3080. Systematic ablation confirms the independent contribution of the attention module, the activity-weighted pixel fusion term, and each BiFPN iteration. Cross-dataset experiments demonstrate that zero-shot transfer retains 84.1% mAP@0.5, rising to 86.0% with 10% target fine-tuning. The results indicate practical night robustness and real-time feasibility, while extreme occlusion and calibration drift remain limiting factors that motivate alignment-aware training and lightweight deployment optimisation as future work.

Increased focus on personalized learning has highlighted the need for real-time monitoring of student engagement. Understanding attention levels during instruction helps improve teaching effectiveness and learning outcomes. However, existing methods rely on manual observation or periodic assessments, which are subjective and lack consistency. These approaches fail to capture moment-to-moment variations in engagement. Conventional systems using basic video tracking or facial detection lack robustness in variable lighting, head pose changes, and classroom dynamics. They are also limited in providing timely, actionable insights. This study presents FocusTrack, a real-time engagement monitoring system that utilizes facial cues and behavioral indicators for accurate classification. The system processes video frames locally and provides continuous engagement feedback. Two annotated datasets—EngageFace (150 hours, classroom-based) and StudyFocus (90 hours, home-based)—were developed to capture diverse learning scenarios. Each dataset includes labels for gaze direction, drowsiness, and facial cues. Experimental results show accuracy levels of 97.0% and 95.5% across the two datasets, outperforming conventional models. The system also maintains latency under 60 ms on CPU- based setups. FocusTrack offers a scalable, privacy-aware solution for continuous engagement monitoring in real-world educational environments. It provides instructors with objective feedback to adapt teaching strategies dynamically.