Abstract
A lightweight Python prototype that uses OpenCV to detect motion in a video stream and pushes Telegram alerts for detected events and handled runtime errors, enabling basic unattended monitoring without a local display.
1. What This Is
This is a small monitoring service that combines computer vision (OpenCV frame analysis) with event delivery (Telegram Bot API). The goal is remote notification rather than local visualization: the process runs headless, watches a video source, and messages a Telegram chat when something moves or when the pipeline itself hits a handled error.
2. How It Works
The loop is straightforward: open a video source, read frames, compute a motion signal, threshold it, and notify. Error handling wraps the loop so a silent crash does not look like "nothing happened."
| # | Stage | Input | Tool | Output |
|---|---|---|---|---|
| 01 | Open source | Configured video/stream URL | OpenCV | Active capture handle |
| 02 | Frame analysis | Consecutive frames | OpenCV | Motion signal value |
| 03 | Event decision | Motion signal + threshold | Python logic | Event flag |
| 04 | Notify | Event flag | Telegram Bot API | Chat message |
| 05 | Error reporting | Handled exception | Telegram Bot API | Error message |
3. Implementation Notes
3.1 Motion signal
OpenCV handles both video capture and the frame-to-frame motion calculation. The threshold that converts the raw signal into a binary "movement detected" event is part of the runtime configuration, not hardcoded in the detection logic.
3.2 Notification channel
Both event alerts and error notifications go through the same Telegram Bot API channel. This keeps setup to a single bot token and chat ID, but it also means an error message and a motion alert look structurally identical in the chat.
3.3 Externalized config
Video source path/URL and Telegram credentials are kept outside the detection code so the same script can point at different cameras or chats without code changes.
4. Constraints
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No deduplication or rate-limiting
A sustained motion event can fire repeated Telegram messages. A production system would need a cooldown window or aggregation.
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Single-stream scope
The prototype watches one video source at a time. Multi-camera monitoring would require a different process or threading model.
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Threshold-based detection only
No ML classifier or object recognition — just a frame-diff signal against a threshold. Lighting changes or small movements can trigger false positives.
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No persistent event log
Detected events exist only as Telegram messages. There is no local timestamped record to query after the fact.
5. Next
- a. Add a cooldown timer and alert deduplication so sustained motion produces one message, not a flood.
- b. Persist events to a lightweight log (SQLite or JSON) with timestamps for post-hoc review.
- c. Extend to multiple camera streams via a worker-per-source model.
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