Spatio-Temporal Active Speaker Tracking, Multi-Pass Semantic Boundary Alignment, and GPU-Scheduled Multimodal Processing for Automated Long-Form Video Understanding

Automated transformation of long-form, multi-speaker horizontal broadcasts (up to 2160p/4K resolution) into semantically self-contained, speaker-centered vertical (9:16, 1080 x 1920) and horizontal (16:9, 1920 x 1080) video segments presents severe systems and algorithmic challenges. Existing pipelines suffer from high decoding latency on modern AV1 and VP9 bitstreams, large language model (LLM) context-window degradation over hour-long transcripts, cross-shot speaker identity drift during rapid camera transitions, and GPU out-of-memory (OOM) failures caused by concurrent neural inference and hardware video encoding. This paper presents a complete nine-stage multimodal video understanding and automated cinematography architecture that resolves these bottlenecks while achieving a 40% reduction in end-to-end processing latency through GPU-accelerated NVENC encoding, asynchronous media decoupling, and VRAM-aware parallel execution scheduling. The architecture is built upon five technical pillars: (i) an asynchronous dual-stream media acquisition subsystem with fast audio-first decoupling and hardware-accelerated H.264 codec normalization; (ii) a fault-tolerant four-tier acoustic transcription engine integrating quantized CTranslate2 inference, cloud API routing, and Root Mean Square (RMS) acoustic energy valley splitting; (iii) a four-pass sliding-window LLM semantic segmentation cascade paired with deterministic word-timestamp boundary snapping and phrase-anchor alignment; (iv) a VRAM-aware hybrid concurrency scheduler that executes parallel FFmpeg worker pools alongside a semaphore-guarded singleton ONNX Runtime session; and (v) a spatio-temporal computer vision engine combining TransNetV2 neural shot boundary detection, perceptual hash layout clustering, InsightFace cosine-similarity identity tracking, clip-scoped pyannote acoustic diarization, and discrete-time Kalman/EMA trajectory stabilization. We further detail multimodal post-processing modules for synthesized audio-visual hooks, dynamic word-group subtitling, integrated loudness profiling at -14 LUFS, and a differential sequence-alignment algorithm for human-in-the-loop transcript editing.