Computer Vision Agents

The Computer Vision Stack

Modern CV agents typically combine multiple capabilities:

┌─────────────────────────────────────────────┐
│              LLM Reasoning Layer            │
│         (GPT-4, Claude, LLaMA)              │
├─────────────────────────────────────────────┤
│           Vision-Language Model             │
│         (GPT-4V, LLaVA, CogVLM)             │
├─────────────────────────────────────────────┤
│        Specialized CV Models                │
│  ┌─────────┬─────────┬─────────┬─────────┐  │
│  │Detection│Tracking │  OCR    │  Face   │  │
│  │ (YOLO)  │ (SORT)  │(EasyOCR)│(InsightF)│ │
│  └─────────┴─────────┴─────────┴─────────┘  │
├─────────────────────────────────────────────┤
│           Video Enhancement                 │
│           (BetterVideo API)                 │
├─────────────────────────────────────────────┤
│              Raw Video Input                │
└─────────────────────────────────────────────┘

Object Detection

Detecting and localizing objects in video frames.

Tools

• YOLO (v8/v9): Fast, accurate, good for real-time
• Detectron2: Facebook's detection library, highly configurable
• Grounding DINO: Open-vocabulary detection (describe what to find)

Integration Pattern

from ultralytics import YOLO

def detect_objects(frames: list) -> list:
    model = YOLO("yolov8x.pt")
    results = []
    for frame in frames:
        detections = model(frame)
        results.append({
            "objects": [d.class_name for d in detections],
            "boxes": [d.bbox for d in detections],
            "confidence": [d.confidence for d in detections]
        })
    return results

# Feed to LLM for reasoning
detections = detect_objects(frames)
response = llm.complete(f"Based on these detections: {detections}, answer: {question}")

Object Tracking

Following objects across frames — essential for temporal reasoning.

Tracking Approaches

• SORT/DeepSORT: Track by detection + appearance features
• ByteTrack: State-of-the-art multi-object tracking
• SAM + tracking: Segment Anything for precise object boundaries

Why Tracking Matters

Without tracking, you can't answer questions like:

• "How long was the person in frame?"
• "Did the same vehicle appear twice?"
• "What path did the object take?"

Tracking assigns persistent IDs across frames, enabling temporal reasoning.

Scene Understanding

Understanding the overall scene, not just individual objects.

Capabilities

• Scene classification: Indoor/outdoor, room type, environment
• Spatial relationships: Object A is left of object B
• Activity recognition: Walking, running, fighting
• Anomaly detection: Something unusual is happening

Vision-Language Models

Modern VLMs (GPT-4V, Claude) can describe scenes directly:

response = vision_model.analyze(
    image=frame,
    prompt="Describe this scene in detail. Include spatial relationships between objects."
)

# Output: "An office environment with a desk on the left, a person seated behind it,
#          and a visitor standing on the right side. There's a window behind the desk..."

Combining CV with LLM Reasoning

The power of CV agents comes from combining perception with reasoning:

# Step 1: Detect objects
detections = yolo.detect(frames)

# Step 2: Track across frames
tracks = tracker.process(detections)

# Step 3: Extract events
events = extract_events(tracks)  # "Person A entered", "Vehicle B stopped"

# Step 4: Reason with LLM
response = llm.complete(f"""
You are analyzing security footage.

Detected objects and tracks:
{tracks}

Events:
{events}

Question: Did anyone enter the restricted area after 10 PM?
""")

The CV layer handles perception; the LLM handles reasoning and language.