CSE445Computer Vision
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M05CO3

Recognition, Detection and Tracking

Multi-object tracking with the Kalman filter, gated assignment, and the SORT family; classical bag-of-features recognition with leakage-safe evaluation; the learning-based vision foundations of convolution arithmetic, backprop, losses, and transfer learning; deep object detection across two- and one-stage models; and segmentation and pose estimation with their losses and metrics.

Explain Kalman filtering, gated assignment, and MOT metrics
Build a bag-of-features model and evaluate it without data leakage
Apply CNN arithmetic, backpropagation, optimization, and transfer learning
Compare detectors and segmentation/pose models by architecture, loss, and metric
OpenCVscikit-learnPyTorchUltralytics

Lectures in this Module(5)

L19

Object Tracking

The Kalman filter, assignment with gating, and the SORT family, scored by MOT metrics.

L20

Classical Object Recognition

Bag-of-features with k-means codebooks, pooling and normalization, an SVM, and leakage-safe evaluation.

L21

Learning-Based Vision Foundations

Convolution arithmetic, backpropagation, losses and optimization, regularization, and transfer learning.

L22

Deep Object Detection

Two-stage and one-stage detectors, anchors and box losses, NMS and NMS-free heads, and AP/mAP.

L23

Segmentation and Pose

Semantic, instance, and panoptic segmentation, their CE/Dice/IoU losses and metrics, and keypoint pose estimation.

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