Motion Detection types (Frame based vs Experimental)

overview bluecherry provides three motion detection algorithms to suit different surveillance environments and requirements each algorithm offers different levels of accuracy, cpu usage, and configuration options motion detection algorithms 🟢 default (original motion detection) best for simple, stable environments with consistent lighting and limited cpu resources characteristics fast and lightweight processing lower cpu usage immediate motion response no configurable sliders (uses default settings) ideal for basic surveillance with just a few cameras how it works uses a single reference frame that slowly adapts to scene changes, comparing each new frame against this background reference 🔵 experimental (basic motion detection) best for simple, stable environments with consistent lighting when you need quick response to movement characteristics compares each video frame against two reference frames one reference updates quickly, the other more slowly triggers motion when changes exceed defined thresholds lower cpu usage than advanced mode immediate motion response available controls frame downscale factor min motion area % max motion area % 🟡 frame based (advanced motion detection) best for environments with changing lighting, background motion (trees, shadows, bugs), or when motion detection accuracy is critical characteristics uses sophisticated temporal analysis across multiple frames continuously updated reference frame with motion event history higher accuracy with fewer false positives adapts better to dynamic environments requires more cpu and memory, but suitable for any modern pc available controls all sliders enabled temporal frame buffer analysis reference frame blending configuration parameters frame downscale factor purpose reduces frame resolution before motion analysis to improve performance options 0 125, 0 25, 0 5, 1 0 how it works a value of 0 5 means the frame is scaled down to 50% of original size before processing impact lower values (0 125 0 25) faster processing, less detail, suitable for high resolution cameras higher values (0 5 1 0) more detail, slower processing, better for low resolution cameras recommendation start with 0 5 for most cameras use 0 25 for 4k cameras or systems with limited cpu miniminal motion area % purpose sets the minimum percentage of the frame that must show motion to trigger detection range 1 100% how it works the system calculates what percentage of the total frame area contains motion only triggers if motion covers at least this percentage practical examples 1 5% detects small objects (people, animals) moving in the distance 5 15% good for detecting people walking through the frame (recommended starting point) 15 30% detects larger movements, reduces false positives from small changes 30%+ only triggers on large objects or multiple people recommendation start with 5 15% for most surveillance scenarios maximum motion area % purpose sets the maximum percentage of frame motion before ignoring it as “too much motion ” range 1 100% how it works if motion covers more than this percentage, it’s considered noise (camera shake, lighting changes) and ignored practical examples 50 70% allows moderate motion before ignoring 70 90% good for stable cameras with occasional large movements 90%+ very permissive, only ignores extreme motion recommendation set to 70 90% for most applications miniminal motion frames (frame based only) purpose sets how many consecutive frames must show motion before triggering an event range 1 255 how it works uses a temporal buffer to track motion across multiple frames only triggers when motion is detected in at least this many frames within the buffer impact lower values (1 5) fast response, more false positives higher values (10 20) more stable detection, fewer false positives recommendation start with 10 15 frames for most environments maximum motion frames (frame based only) purpose sets the size of the frame buffer used for temporal analysis range 1 255 how it works this is the “ring buffer” size that stores the motion history of recent frames for temporal analysis impact smaller buffer (10 20) faster response, less temporal context larger buffer (20 50) better temporal analysis, more memory usage recommendation set to 20 30 frames for most applications reference frame blending ratio (frame based only) purpose controls how quickly the reference frame adapts to changes in the scene range 2 100 how it works the reference frame is continuously updated by blending new frames with the existing reference this ratio determines the blend percentage impact lower values (2 20) slow adaptation, better for stable environments higher values (20 50) fast adaptation, better for dynamic environments recommendation start with 10 20 for most environments motion sensitivity grid the motion sensitivity grid provides preset sensitivity levels that affect the motion detection thresholds off motion detection disabled minimal only detects large, significant movements low detects moderate to large movements average balanced sensitivity for most scenarios (recommended starting point) high detects smaller movements and subtle changes very high maximum sensitivity, detects even very small movements note these sensitivity levels are about motion detection threshold sensitivity , not object size high sensitivity detects smaller movements, while low sensitivity only detects larger movements configuration recommendations for indoor surveillance algorithm basic or frame based min motion area 5 10% max motion area 70 80% frame downscale 0 5 sensitivity average to high for outdoor surveillance algorithm frame based min motion area 10 20% max motion area 80 90% frame downscale 0 25 0 5 min motion frames 15 20 sensitivity low to average for high traffic areas algorithm frame based min motion area 15 25% max motion area 85 95% min motion frames 20 25 sensitivity low for low light environments algorithm frame based min motion area 8 15% max motion area 75 85% reference frame blending 15 25 sensitivity average troubleshooting too many false positives increase min motion area % increase min motion frames (frame based) lower sensitivity level increase max motion area % missing important events decrease min motion area % decrease min motion frames (frame based) increase sensitivity level switch to frame based algorithm high cpu usage decrease frame downscale factor switch to cv basic algorithm reduce max motion frames (frame based) poor performance in changing lighting switch to frame based algorithm adjust reference frame blending ratio increase min motion frames debug features enable debugging snapshots when enabled, the system saves motion detection debug images to help troubleshoot configuration issues these images show motion detection areas reference frame comparisons threshold analysis location debug images are typically saved to /tmp/ or the system’s temporary directory best practices start with defaults and adjust based on your specific environment test thoroughly in your actual surveillance environment monitor cpu usage and adjust frame downscale factor if needed use frame based for environments with changing lighting or background motion enable debug snapshots temporarily when fine tuning settings consider camera placement avoid pointing at moving objects like trees or flags regular maintenance review and adjust settings as environmental conditions change