The operations and analytics teams needed an efficient way to visualize vehicle movement patterns captured by a third‑party CCTV provider. Although the provider offered APIs to pull snapshots of detected vehicles, the process was manual, time‑consuming, and inconsistent.
Key issues included:
- No automated mechanism to fetch vehicle snapshots from the third‑party CCTV system
- Large volume of images made manual downloading impractical
- Lack of a streamlined workflow to convert thousands of images into time‑lapse videos
- Difficulty in quickly reviewing long-duration footage to identify patterns, peak hours, or anomalies
- High dependency on manual intervention, increasing processing time and human errors
The absence of automation limited the team’s ability to analyze trends or produce visual evidence quickly.
We developed an end‑to‑end automated script that:
- Connects with the third‑party CCTV service provider via their APIs
- Retrieves all snapshots (SS) of detected vehicles within a selected time range
- Organizes and stores the images in chronological order
- Automatically generates time‑lapse videos from the collected vehicle snapshots
- Provides a reusable, scheduled automation pipeline for daily/weekly processing
The result is a fully automated time‑lapse creation system that converts raw images into visual insights.
A. API Integration & Snapshot Retrieval
- Integrated with the third‑party CCTV provider’s REST APIs
- Implemented token‑based authentication to ensure secure access
- Automated retrieval of snapshots based on parameters like:
- Date/time range
- Camera ID
- Detected object type (vehicle)
Implemented retry logic and pagination to handle network fluctuations or large datasets
B. Image Management & Processing
- Stored downloaded images in structured folders sorted by timestamp
- Validated images for corruption, size, and sequence correctness
- Applied optional preprocessing such as:
- Image resizing
- Quality enhancement
- Removing duplicates or near-identical frames
C. Automated Time‑Lapse Generation
- Used a video processing engine (FFmpeg or equivalent library) within the script
- Assembled chronological images into a time‑lapse video at configurable FPS (e.g., 15 or 24 fps)
- Embedded timestamps or overlays on each frame if required
- Exported high‑quality MP4/MOV outputs ready for review or reporting
D. Scheduling & Automation
Added support for:
- Daily scheduled runs
- Automatic cleanup of old images
- Notification when video generation is complete
Ensured script can run on cloud servers, local machines, or containerised environments
E. Reporting & Traceability
Maintained logs for:
- API request success/failure
- Total images fetched
- Video creation status
- File paths of generated output
Enabled quick debugging and auditability.
Operational Efficiency
- Reduced manual image retrieval effort by 100%
- Cut video creation time from hours to minutes
- Enabled the team to generate daily time‑lapse summaries effortlessly
Enhanced Analytics
- Provided clear visual patterns of vehicle movement
- Improved ability to identify congestion times, anomalies, or peak traffic moments
- Enabled faster decision‑making through visual insights
Scalability & Reliability
- Capable of handling large volumes of snapshots from multiple cameras
- Easily extendable to include additional object types in the future
- Fully automated pipeline ensures consistency and accuracy
Cost Savings
- Eliminated manual data collection efforts
- Reduced reliance on video storage by using lightweight image-based time‑lapse outputs
The automated time‑lapse video creation system transformed how the team utilized CCTV snapshot data. By integrating directly with the provider’s APIs, systematically downloading vehicle images, and generating high‑quality time‑lapse videos, the solution dramatically improved efficiency, visibility, and decision-making capabilities. This automation not only saved time and effort but also provided a scalable, repeatable framework for continuous video analytics.
