What is video analytics and how is it different from computer vision?
Computer vision is the broader discipline of extracting structured information from images. Video analytics is computer vision applied to video sequential frames over time where the temporal dimension enables capabilities not possible on still images: object tracking (following the same person or vehicle across many frames), action recognition (detecting behaviours that unfold over seconds), crowd flow analysis (movement patterns over time), and anomaly detection in operational sequences. The added technical complexity of video analytics vs image analysis: managing streaming data (continuous frames at 25-60 FPS), efficient frame processing (cannot run a full detection model on every frame at camera resolution), and temporal modelling (understanding what happens across frames, not just within a single frame).
Can video analytics work with our existing CCTV cameras?
Yes most modern IP cameras stream video via RTSP (Real-Time Streaming Protocol) or ONVIF (Open Network Video Interface Forum), which our video analytics infrastructure connects to directly without requiring hardware replacement. Camera requirements for good analytics accuracy: minimum 720p resolution (1080p preferred for ANPR), adequate lighting (low-light cameras with IR illumination for 24/7 monitoring), appropriate field of view for the use case (entrance counting requires a top-down or angled view of the doorway; product zone monitoring requires a view of the shelf area). ClickMasters reviews camera specifications and placement as part of the scoping engagement recommending any camera upgrades needed for the target accuracy, and designing analytics systems around existing hardware where possible.
How do you handle privacy compliance in video analytics?
Privacy-preserving video analytics is a design principle, not an afterthought. For retail and workplace analytics, ClickMasters systems output aggregate metrics and anonymised trajectories not facial recognition or persistent individual identification. Technically: object tracking assigns temporary IDs per session (ID does not persist across camera views or days), bounding box blurring (faces and identifying features blurred in any stored video clips), no biometric data collection, aggregate dashboard outputs (counts, heatmaps, density maps no individual-level data), and data retention policies (raw video not stored beyond the minimum required buffer; derived analytics data retained per policy). GDPR compliance: processing is based on legitimate interest for operational safety and efficiency, with appropriate signage notifying visitors. ClickMasters documents the data processing activities for GDPR Article 30 records.
What hardware is needed for real-time video analytics?
Hardware requirements depend on the number of camera streams and the complexity of the analytics. Rule of thumb: an NVIDIA RTX 4090 GPU or equivalent can process 10-20 camera streams simultaneously with YOLO v8 detection at 25 FPS. For enterprise deployments: NVIDIA A10 or A30 in an on-premises server handles 30-50 streams. For edge deployment (analytics running at the camera without cloud round-trip): NVIDIA Jetson Orin (AGX model 200+ TOPS) handles 5-10 streams on-device with sub-100ms latency appropriate for low-connectivity environments or high-security sites where video cannot leave the premises. Cloud deployment (AWS EC2 G5 instances) is appropriate when the analytics result (event flags, counts) is less sensitive than the raw video, and network bandwidth to the cloud is available. ClickMasters recommends the hardware configuration that meets the latency and privacy requirements at minimum cost.
