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VideoHighlighter is an open-source desktop application that finds and explains the strongest moments in raw video without uploading anything. It scores footage second by second across scene changes, motion peaks, audio levels, detected objects, recognized actions, and transcript content, then surfaces a signal timeline and a self-contained HTML report showing exactly why each moment was kept — down to the per-signal point breakdown and confidence tier.

What sets it apart from typical "AI highlights" tools is that every run explains its own arithmetic rather than handing back an opaque score. The report separates what the detectors actually observed from what the transcript merely claimed, flags moments that scored well but were still cut, and offers concrete next steps grounded in that run's own numbers. A composition-rules engine lets users define what a combination of detections means for their footage — for example, a ball whose center falls inside a hand-labeled net region counts as a "goal" — without retraining a model or waiting on a fresh detection pass, since rules re-read detections that already exist.

The project ships two front ends over one Python engine: a PySide6 desktop GUI with a full timeline viewer, and a newer Tauri/React web-style shell backed by a FastAPI sidecar. An "Auto" pipeline can ingest an entire camera card by layout (correctly reordering GoPro's chapter-before-file-number naming), run detection, cut a reel, and lay down a beat-synced music bed as one resumable job.

VideoHighlighter is free and AGPL-3.0 licensed for local, offline use — object detection, action recognition, audio analysis, transcript generation, CLIP-based visual search, and the composition engine are all included with no license key. A separate closed-source Pro edition adds real-time overlays, teach-by-example categories, and open-vocabulary detection as a paid upsell, positioned as an offline alternative to cloud tools like Twelve Labs, Runway, and Descript.