Video analysis extends computer vision techniques to the temporal domain, enabling understanding of motion, events, and activities in video sequences. This section explores methods for analyzing and extracting information from video data.
Motion Detection and Tracking #
Motion detection identifies moving objects in video sequences, while tracking follows these objects over time.
Motion Detection Techniques #
- Background Subtraction: Comparing frames against a background model
- Simple frame differencing
- Running average
- Gaussian Mixture Models (GMM)
- MOG2 (Mixture of Gaussians)
- Optical Flow: Estimating pixel motion between frames
- Lucas-Kanade method
- Horn-Schunck method
- Farnebäck algorithm
- Deep Learning Approaches: CNN-based motion detection
Object Tracking Methods #
- Point Tracking: Following feature points
- Kanade-Lucas-Tomasi (KLT) tracker
- Kernel Tracking: Following regions
- Mean Shift
- CAMShift (Continuously Adaptive Mean Shift)
- Silhouette Tracking: Following object contours
- Contour tracking
- Shape matching
- Deep Learning Trackers:
- SORT (Simple Online and Realtime Tracking)
- DeepSORT
- SiamRPN
- FairMOT
Challenges #
- Occlusion: Objects becoming partially or fully hidden
- Illumination Changes: Varying lighting conditions
- Complex Backgrounds: Cluttered or dynamic scenes
- Scale and Viewpoint Changes: Objects changing size or orientation
- Real-time Performance: Processing video at frame rate
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