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Recordings are stored as time-stamped segments on the server. When you pick a camera, a single WebSocket channel pushes the list of recorded periods and the stream of detection events, so the timeline fills without polling. Events come from the analytics database, where the AI pipeline wrote every motion, person, vehicle and plate detection with a thumbnail. The filter chips narrow the list by type. The server decrypts segments in memory when needed and serves the video to the browser. Clicking an event moves the playhead to that moment.

While the AI pipeline watched the live stream, it stored each detection with its class, confidence, bounding box and a thumbnail in the analytics database. Playback reads those rows for the moment being shown and draws the box over the video in the browser, so nothing is burned into the recording. The side list is fed by the same rows and shows class, time and confidence. Clicking an entry seeks the recording to that instant. The tracker also counts how many sightings belong to one object, which helps separate one parked car from many passing ones.

Inspect takes the frame under the playhead and sends it to the server, which runs the detector on it and cuts out the object with its mask. The Identify button passes that crop to a vision language model configured in the AI settings, running locally or through a provider you choose, and shows its description. Colour comes from the crop itself. Search this type and colour then queries the analytics database for detections of the same class and colour across the recordings. The description is a model's best guess, and it says so when the view is poor.

The server walks the recorded segments for the chosen range, decrypting them in memory, and samples frames. A background-subtraction gate skips empty frames, so the object detector and tracker only run where something moves. Each tracked object becomes a tube of cropped frames with its original timestamps. Tubes are rescheduled to play at the same time over one shared background, and ffmpeg renders the result as an MP4 with each object's real time burned in. The job runs in the background with progress polling and can be cancelled. Clicking an object jumps the timeline to its original moment.

The server cuts the requested time range from the recorded segments with ffmpeg, decrypting encrypted recordings in memory if you ask. Timestamp, camera name and watermark are burned into the picture. If you enable encryption, the MP4 is encrypted with AES-256 using a key derived from your password by PBKDF2, and a signature file is written beside it. The password is never stored, so nothing can recover it. The result stays on the server, downloads to your computer, or both, depending on the destination you choose.

The player runs on any computer without a PotoP server. You choose the encrypted video and its signature file, and enter the password. It first checks the signature with an HMAC to prove the file is unchanged, then derives the key from the password with PBKDF2 and decrypts with AES-256. The clear video exists only in memory while it plays. From there you can drag the green handles to trim a selection, save a still picture, or export a plain MP4 copy. A browser-based variant ships inside export folders with launchers for Linux, Windows and macOS.

Each camera has its own recording settings stored on the server, and the recorder applies them as soon as you save. Triggers can be continuous, a weekly schedule, or events such as motion, person, plate, an alarm input or a macro, with pre-event and post-event buffers so the moment before the trigger is kept. Segments are cut at a set length and can be encrypted with AES-256 after they are closed. Retention removes old segments by age or by maximum size. Start, stop and detection events can raise notifications through system events, email, Telegram or a webhook.

Every detection produced by the AI pipeline is written to the analytics database with camera, class, confidence, time, a track ID and attributes. The search screen runs a filtered query against that database, so it answers in one step across all cameras. Attributes such as colour, clothing and hair are derived by classifying the object's crop at detection time. Double-clicking a row opens Playback at that camera and time. The Export CSV button writes the current result list to a file, and the statistics box counts persons, vehicles and unique tracks.

Each person or vehicle crop is turned into a 256-value appearance vector by a small re-identification model running on OpenVINO, and stored in the analytics database. Searching embeds the query picture the same way and compares it with the stored vectors by cosine similarity, using an in-memory index so the result is exact and quick. Because it matches overall appearance and not a face, it works on people seen from behind or at low resolution. Selecting a result loads that track's sighting history and route, and Follow in Playback opens the recordings.

The picker lists all user groups with a short description of what each is allowed to do, and a search field narrows the list. Adding a group stores a link between that user group and the device group on the server. From then on, every member of that group gets the group's cameras in live view, playback and layouts, and no others through this link. Linking and unlinking is one step and takes effect for signed-in users without a restart.