13 Motion, tracking and event-based vision
Chapter status: Structural placeholder.
This chapter currently contains the approved heading structure but no reviewed technical content.
13.1 Motion representation
13.1.1 The brightness-constancy assumption
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13.1.2 Sparse vs dense motion fields
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13.2 Optical flow
13.2.1 The Lucas–Kanade method
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13.2.2 The Horn–Schunck method
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13.2.3 Dense flow: Farnebäck and pyramidal schemes
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13.2.4 Learned optical flow (outline)
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13.3 Background modelling and change detection
13.3.1 Frame differencing
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13.3.2 Gaussian mixture models
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13.3.3 Robust background subtraction
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13.4 Object tracking
13.4.1 Template and feature tracking
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13.4.2 Mean-shift and correlation-filter trackers
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13.4.3 Kalman filters and particle filters
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13.4.4 Multi-object tracking and data association
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13.5 Event-based vision
13.5.1 The event-camera imaging principle
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13.5.2 Representations: event frames, time surfaces, voxel grids
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13.5.3 Algorithms on event streams
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13.5.4 Industrial fit and integration considerations
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