> For the complete documentation index, see [llms.txt](https://docs.degirum.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.degirum.com/face-recognition/guides/overview-2.md).

# Face Clip Manager

Helper class for managing video clips saved by FaceTracker.

## What is FaceClipManager?

`FaceClipManager` manages video clips saved by `FaceTracker`'s alert recording pipeline. It provides simple methods to list, download, and remove clips from object storage.

Typically used with the same `clip_storage_config` from your FaceTracker setup.

## When to Use

Use `FaceClipManager` to manage clips saved by FaceTracker:

* List alert clips saved during monitoring
* Download clips for backup
* Implement retention policies (remove old clips)
* Clean up storage after review

**Typical workflow:**

1. Configure `FaceTracker` with `clip_storage_config` and alert mode
2. Run `FaceTracker.start_face_tracking_pipeline()` - saves clips when alerts trigger
3. Use `FaceClipManager` to list/download/remove saved clips

**For analyzing clips**, use [FaceTracker.find\_faces\_in\_clip()](/face-recognition/guides/overview-1/methods.md#find_faces_in_clip).

***

## How It Works

Initialize `FaceClipManager` with an `ObjectStorageConfig` - typically the same config used by FaceTracker:

```python
import degirum_face
import degirum_tools

# Use same storage config as FaceTracker
clip_manager = degirum_face.FaceClipManager(
    degirum_tools.ObjectStorageConfig(
        endpoint="./security_clips",
        bucket="unknowns"
    )
)

# List all clips
clips = clip_manager.list_clips()

# Download a clip
clip_data = clip_manager.download_file("incident_01.mp4")

# Remove old clips
clip_manager.remove_file("old_clip.mp4")
```

See [Configuration Guide](/face-recognition/guides/overview-2/configuration.md) for storage setup and FaceTracker integration.

See [Methods Reference](/face-recognition/guides/overview-2/methods.md) for complete API with examples.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://docs.degirum.com/face-recognition/guides/overview-2.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.