Merge remote-tracking branch 'origin/master' into dev

Resolve conflicts in the export pipeline where dev's job-queue refactor
met master's chapter-metadata and security work.

- Unify chapter support under ChaptersEnum (none / recording_segments /
  review_items); the realtime stream-copy export selects the per-segment
  or per-review-item builder by the camera's configured mode. Thread
  chapters through ExportRecordingsBody -> _build_export_job -> ExportJob
  -> RecordingExporter.
- Keep master's creation_time/comment export metadata and fix a
  video_path duplication the textual merge introduced in the preview
  command.
- Move the chapters request field to ExportRecordingsBody (the single
  export endpoint) where it is actually honored.

Restore security fixes the automatic merge would have reverted:
- frigate/util/services.py: restore the #23493 rename to the public
  is_go2rtc_arbitrary_exec_allowed so create_config.py's dynamic-source
  exec guard imports and runs (the merge otherwise left a broken import).
- Preserve the export image-path ".." traversal check inside
  _sanitize_existing_image, applied to single/custom/batch exports.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

docs/docs/troubleshooting/faqs.md

@@ -124,3 +124,19 @@ cameras:
       width: 1280
       height: 720
 ```
+
+### Why does Frigate keep creating new events for my parked car?
+
+Stationary tracking is designed to _prevent_ this — a parked car should stay one tracked object and not generate new events. If you're getting repeated events for the same car, it's likely that Frigate is losing the tracked object and re-detecting it as a new one.
+
+Open one of the events in Explore → **Tracking Details**. If the detection scores are low (< 70% or so), the model isn't confident the parked car is a car. This is common with the free [COCO-trained](https://cocodataset.org/#explore) object detection models on steep/top-down angles, partially occluded cars, foliage, or low-light footage. When detections fall below `min_score` for too many frames the tracker loses the object, and the next confident frame creates a brand new one.
+
+What helps:
+
+- **Improve the view** — even a small angle change that gets more of the car visible could lift scores enough to stabilize tracking.
+- **Use a more accurate model** — switching from `mobiledet` to `yolov9`, or stepping up to a larger variant like `yolov9-s` over `yolov9-t`, can help (at the cost of inference time, and still on the COCO dataset). The biggest gains usually come from fine-tuning a model on images from your own cameras so it learns your specific scene. [Frigate+](https://frigate.video/plus) is a paid option that does this - models are trained on security-camera footage and can be fine-tuned on images you submit from your own setup.
+- **Don't set `detect -> stationary -> max_frames` for `car`** — it artificially ends tracking and forces re-detection as a new object. See [Stationary Objects](../configuration/stationary_objects.md).
+- **Restrict alerts to the areas you care about** with `required_zones` — see [Zones](../configuration/zones.md#restricting-alerts-and-detections-to-specific-zones). Make sure those zones use the default `loitering_time: 0` unless you specifically want the review item to stay open until the car leaves.
+- **Filter impossible locations** with [object filter masks](../configuration/masks.md#object-filter-masks) if cars are being detected on rooftops, treetops, etc.
+
+See [Object Filters](../configuration/object_filters.md) for more on tuning `min_score` and `threshold` — note that raising them too high will make this exact problem worse.