Detect objects using Triton-served models

Use This When

• Building surveillance systems that need to locate and classify objects in video feeds
• Implementing retail analytics for people counting, queue detection, or product recognition
• Creating safety monitoring pipelines for PPE detection, vehicle detection, or hazard identification
• Preparing bounding boxes for downstream tracking, segmentation, or attribute analysis

What It Does

• Detects objects using Triton-served models with configurable preprocessing and postprocessing
• Returns bounding boxes with class labels and confidence scores filtered by threshold
• Applies Non-Maximum Suppression to eliminate duplicate detections
• Supports flexible model configurations: YOLO, Faster R-CNN, SSD, or custom architectures

Works Best With

• Video inputs → this component → track-object for persistent ID assignment and motion analysis
• Detections → detect-bodypart or segment-masks for detailed region analysis
• Person boxes → detect-landmarks for pose estimation and gesture recognition
• Integration with check-object-distance or detect-zone-transition for spatial analytics

Caveats

• Model choice determines accuracy and speed; balance detection quality with inference latency
• Small or occluded objects require specialized models trained on similar conditions
• Class filtering and NMS thresholds significantly affect precision/recall tradeoff
• Preprocessing parameters (mean, std, color_model) must match model training configuration