Object tracking basics in Computer Vision - Model Pipeline Trace

Model Pipeline - Object tracking basics

This pipeline follows how a video frame is processed to detect and track a moving object over time. It shows how frames are prepared, features are extracted, the tracking model updates the object's position, and how accuracy improves as the model learns the object's movement.

Data Flow - 6 Stages

1Input video frames

30 frames x 480 height x 640 width x 3 color channels→Capture raw video frames from camera→30 frames x 480 height x 640 width x 3 color channels

Frame 1: RGB image of a person walking

↓

2Preprocessing

30 frames x 480 x 640 x 3→Resize frames to 224x224 and normalize pixel values to 0-1→30 frames x 224 height x 224 width x 3 channels

Frame 1 resized and pixel values scaled between 0 and 1

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3Feature extraction

30 frames x 224 x 224 x 3→Extract features using a CNN backbone→30 frames x 7 x 7 x 512 feature maps

Frame 1 feature map highlights edges and shapes of the person

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4Object detection

30 frames x 7 x 7 x 512→Detect bounding boxes of objects in each frame→30 frames x variable number of boxes x 4 coordinates

Frame 1 detected box: [x=50, y=100, width=80, height=160]

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5Tracking model update

30 frames x variable boxes x 4→Match detected boxes frame-to-frame to track object movement→30 frames x 1 tracked object bounding box x 4

Tracked object box moves from [50,100,80,160] to [55,105,80,160]

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6Output tracked positions

30 frames x 1 x 4→Output tracked bounding box coordinates per frame→30 frames x 4 coordinates

Frame 1: [50,100,80,160], Frame 2: [55,105,80,160]

Training Trace - Epoch by Epoch

Loss
1.0 |***************
0.8 |**********     
0.6 |*******        
0.4 |****           
0.2 |**             
0.0 +--------------
      1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.85	0.60	Initial training with random weights, loss high, accuracy low
2	0.65	0.72	Model starts learning object features, loss decreases, accuracy improves
3	0.50	0.80	Better tracking of object movement, loss continues to drop
4	0.40	0.85	Model stabilizes, tracking accuracy improves steadily
5	0.35	0.88	Final epoch shows good convergence with low loss and high accuracy

Prediction Trace - 5 Layers

Layer 1: Input frame preprocessing

Layer 2: Feature extraction CNN

Layer 3: Object detection

Layer 4: Tracking update

Layer 5: Output tracked position

Model Quiz - 3 Questions

What is the main purpose of resizing and normalizing video frames before tracking?

A To add color filters to the frames

B To make the data easier for the model to process

C To increase the frame size for better detail

D To remove objects from the frames

Key Insight

Object tracking combines detecting objects in each frame with linking those detections over time. Preprocessing helps the model focus on important features. Training improves the model's ability to predict object positions accurately, shown by decreasing loss and increasing accuracy. Tracking ensures smooth following of objects across frames.