The short definition

Object detection is a computer vision task: given an image, find each instance of an object of interest, draw a bounding box around it, and label it with a class (person, car, truck) and a confidence score. Modern object detection uses convolutional neural networks (YOLO, SSD, Faster R-CNN family) trained on millions of labeled images, running inference on each video frame to feed downstream analytics. For security, it's the foundation: almost every other analytic (line crossing, intrusion detection, dwell time, loitering, heat map) starts with detected objects and reasons about their position, trajectory, or behavior over time. A camera without object detection can do simple motion alerts but can't filter noise or generate the structured event data modern security operations need.

Where object detection runs

  • On the camera (edge AI). Modern cameras from Axis (ARTPEC-8), Hanwha (Wisenet 7 SoC), Avigilon (H5A), Verkada, and Hikvision (NDAA-blocked) ship with built-in inference accelerators. Person and vehicle detection runs in real time on the camera with no external dependency. Latency: milliseconds.
  • On the NVR. Some enterprise NVRs (Hanwha Wisenet WAVE, Avigilon HD NVR) have GPUs that run detection on incoming streams, useful for cameras without onboard AI.
  • On a dedicated analytics server. Camera-agnostic platforms (Briefcam, Genetec KiwiVision, Dragonfruit AI, Intenseye) run detection on multiple streams. Used when cameras lack onboard AI or the analytics need is complex.
  • In the cloud. Verkada Command, Avigilon Alta, and Eagle Eye Networks run detection in the cloud. Higher latency than edge but more flexibility for model updates.

Common detection classes for security

  • Person. The base class. Filters animal, vehicle, and noise events from human-relevant alarms. See the person-detection entry in the main glossary.
  • Vehicle. Often broken into car, truck, bus, motorcycle, bicycle. Used for parking-lot occupancy, vehicle entry/exit logging, and dwell-time analytics.
  • Weapon. Firearm and edged-weapon classes. Specialty analytics from Athena Security, ZeroEyes, and Actuate AI focus on weapon detection in real time. Used in K-12 schools, courthouses, and high-risk retail.
  • Package. Used in retail and warehouse for theft prevention and shipping verification.
  • PPE compliance. Hardhat, high-vis vest, gloves, safety glasses. Standard in manufacturing and construction.
  • Animals. Dog and cat classes filter pet movement from intrusion alarms in residential and small-commercial.

Where object detection drives ROI

  • Perimeter intrusion detection. Outdoor alarms filtered to person-classified events only. Cuts false-dispatch rates by 60 to 90 percent. Pairs with verified monitoring for the residual events.
  • Construction-site after-hours. Person detection at otherwise-unattended sites. Voice-down deterrence triggered by detection events. Replaces guard-force overnight coverage at significantly lower cost.
  • Retail loss prevention. Person-plus-package detection at exits, dwell time at high-shrink fixtures, after-hours intrusion. Generates the alarm queue loss prevention staff actually review.
  • Workplace safety. PPE-compliance detection at manufacturing entrances, forklift-pedestrian proximity alarms in warehouses. OSHA documentation and incident reduction.
  • Healthcare patient safety. Person-fallen-on-floor detection in patient rooms and corridors. Pairs with nurse-call workflows.

Accuracy considerations

Beyond the per-class accuracy ranges, real-world results depend on a few install factors. Camera resolution and quality: 4MP at 15fps with WDR is a strong baseline, and higher resolution improves detection of small or distant objects. Placement: models are trained on certain viewing angles (typically 6 to 15 feet mounting height, slight downward angle), so extreme angles like overhead fisheye or low-mount under 5 feet reduce accuracy. Lighting: detection handles low light reasonably, but full darkness with no IR illumination defeats most models, so pair it with low-light or thermal cameras outdoors. Model freshness: cloud models update automatically and edge-AI cameras update via firmware push, and firmware older than 12 months often misses accuracy improvements.

When to ask Tec-Tel about object detection

Object detection is the right answer when motion-only alarms are creating false-dispatch fees or operator fatigue. We inventory existing cameras, identify which support edge AI, and scope retrofits with camera-agnostic analytics where needed.