🎬 Behavior_Recognition

Behavior Recognition: Teaching Machines to Understand What Cats Are Doing

Washin Village AI Director Tech Notes #5

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🎯 From "Who Is This" to "What Are They Doing"

After AI can identify whether it's Jelly or Ariel, what's next?

Behavior Recognition: Teaching AI not just to identify animals, but to understand what they're doing.

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📊 Behavior Categories We Defined

Behavior	Description	Examples
resting	Lying down, sleeping	Cat napping
walking	Moving around	Walking through room
eating	Eating, drinking	At food bowl
sitting	Sitting posture	Sitting upright
playing	Chasing, playing with toys	With toys or other cats
standing	Standing and looking	Alert stance

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💻 Technical Approaches

Approach 1: Single-Frame Classification

Classify behavior for each image frame.

class BehaviorClassifier:
    def __init__(self, model_path):
        self.model = YOLO(model_path)

    def predict(self, frame):
        result = self.model.predict(frame)
        return result.class_name, result.confidence

Pros: Simple, fast
Cons: Can't judge continuous actions ("walking" vs "stopped")

Approach 2: Sequence Analysis

Analyze multiple consecutive frames to understand dynamic behavior.

class SequenceBehaviorAnalyzer:
    def __init__(self, window_size=10):
        self.window_size = window_size
        self.frame_buffer = []

    def analyze(self, frames):
        predictions = [self.classify_frame(f) for f in frames]

        # Detect behavior transitions
        transitions = self.detect_transitions(predictions)

        # Return primary behavior
        return self.majority_vote(predictions)

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📈 Data Preparation

Labeling Process

1. Screenshot from videos: 200+ images per behavior category
2. Manual labeling: Using Label Studio
3. Quality review: Ensure labeling consistency

Data Statistics

Behavior	Training	Validation
resting	250	65
walking	180	45
eating	150	38
sitting	200	50
playing	120	30
standing	144	48
Total	1044	276

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🔧 Training Configuration

from ultralytics import YOLO

model = YOLO('yolov8s-cls.pt')

model.train(
    data='behavior_dataset',
    epochs=50,
    imgsz=224,
    batch=32,

    # Augmentation settings
    augment=True,
    degrees=10,
    translate=0.1,
    scale=0.2,
    fliplr=0.5,
)

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📊 Expected Results

Metric	Target
Top-1 Accuracy	75%+
Inference Speed	<20ms
Use Case	Real-time video analysis

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🎬 Real Applications

1. Automatic Video Classification

Input Video → Behavior Recognition → Auto-tagging
                       ↓
              "Jelly sleeping" "Dollar eating"

2. Smart Editing

Automatically cut highlights based on behavior:

• "Playing" clips → For funny videos
• "Resting" clips → For relaxing videos

3. Health Monitoring

Long-term tracking of animal behavior patterns:

• Decreased eating frequency → Possible illness
• Reduced activity → Needs attention

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💡 Lessons Learned

1. Clear behavior definitions: Vague definitions lead to inconsistent labeling
2. Balanced dataset: Keep sample counts similar across categories
3. Consider continuity: Single-frame has limits; sequence analysis is more accurate
4. Scene diversity: Include samples from different lighting and angles

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🔮 Future Development

• Fine-grained behaviors: Distinguish "fast running" from "slow walking"
• Interaction detection: Two cats playing together
• Abnormal behavior: Detect fighting or illness signs

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Washin Village 🏡 by AI Director