Hi everyone,
I’m a software engineering student working on a short-term AI/computer vision project (≈2 months), and I’d really appreciate feedback from people with experience in OpenCV or real-world deployments.
The original proposal was to use a camera feed to detect whether office workers are “working” or “wasting time” (e.g., sitting at desks vs walking around).
After doing some research, I realized that the problem statement itself is false
• “Working” vs “wasting time” is subjective and hard to define So I’m reframing the problem to
Build a privacy-aware office occupancy & activity analytics system, NOT a productivity evaluator.
The system would:
• Detect people in an office environment • Track basic activity states (e.g., sitting, standing, moving) • Produce aggregate statistics (occupancy over time, sitting vs standing ratios, movement peaks) • Leave interpretation to management instead of the model making judgments No identity recognition, no face recognition
YOLOv8-Pose for posture (sitting vs standing)
• OpenCV for video processing • Basic tracking (e.g., ByteTrack / DeepSORT) • Backend with Flask/FastAPI • Simple dashboard for visualization (counts, charts) Video input could be:
• Webcam feed Questions
1. Is this reframed problem realistic to implement well in 2 months? 2. Would YOLOv8 (+ pose) be sufficient, or would you recommend a different approach? 3.where can i find data of photage of people working in office
Thanks in advance!
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