Computer Vision, Explained.
A living knowledge publication dedicated to computer vision.
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All articles →Motion & Tracking
A New Approach to Linear Filtering and Prediction ProblemsRudolf Kalman's 1960 paper that recast Wiener's filtering problem in state-space form and solved it with a recursive estimator, the origin of the Kalman filter.
Motion & Tracking
An Iterative Image Registration Technique with an Application to Stereo VisionThe 1981 IJCAI paper by Lucas and Kanade that replaced exhaustive search in image registration with a gradient-guided Newton–Raphson-type iteration, the origin of the Lucas–Kanade method.
Motion & Tracking
Aperture ProblemWhy motion seen through a small window is ambiguous along edges, so that only the component of motion across an edge can be measured locally.
Motion & Tracking
Brightness Constancy AssumptionThe assumption that a scene point keeps the same image intensity as it moves between frames, which turns motion estimation into an intensity-matching problem.
Motion & Tracking
Coarse-to-Fine EstimationEstimating large motions with small-motion methods by solving on an image pyramid, from the coarsest level to full resolution, and refining the estimate at each level.
Motion & Tracking
Data AssociationDeciding which measurements or detections belong to which tracked targets, and which are false alarms, missed detections, new targets, or targets that have disappeared.
Motion & Tracking
Determining Optical FlowThe 1981 paper by Horn and Schunck that computed dense optical flow by combining the brightness change constraint with a global smoothness assumption, founding the variational approach to motion estimation.
Evaluation & Metrics
Endpoint ErrorThe standard accuracy measure for optical flow, the distance in pixels between an estimated flow vector and the true one, averaged over the image.
Motion & Tracking
Feature TrackingHow distinctive image points are selected and followed across video frames, using the classic KLT tracker as the main example.
Motion & Tracking
Horn–Schunck MethodA global variational method that computes dense optical flow by minimizing brightness constancy errors together with a penalty on spatial variation of the flow, solved by a simple iterative averaging scheme.
Motion & Tracking
Hungarian AlgorithmAn algorithm that finds the minimum-cost one-to-one matching between two sets, used in computer vision to match detections to tracks and predictions to ground truth.
Image Processing & Computational Photography
Image GradientsHow image gradients measure the direction and strength of intensity change, and why they underpin edges, corners, and motion estimation.
Motion & Tracking
Kalman FilterA recursive algorithm that estimates the hidden state of a linear dynamic system from a sequence of noisy measurements, widely used to smooth and predict object positions in tracking.
Motion & Tracking
Lucas–Kanade MethodA local, gradient-based method that estimates the displacement of an image window by assuming constant motion within it and solving a small least-squares problem, iterated with warping.
Motion & Tracking
Motion EstimationRecovering how image content, objects, or the camera moved from a sequence of images, from per-pixel flow to global and 3D motion.
Motion & Tracking
Multi-Object TrackingEstimating the trajectories of a varying, unknown number of objects in a video while keeping each object's identity consistent over time.
Motion & Tracking
Object TrackingEstimating the position, extent, or state of one or more objects in every frame of a video, keeping each object's identity over time.
Motion & Tracking
Optical FlowThe apparent motion of image content between two frames, represented as a two-dimensional displacement at every pixel.
Motion & Tracking
Optical Flow EstimationComputing a dense field of pixel displacements between two video frames, from classical variational methods to learned networks such as RAFT.
Motion & Tracking
RAFTA deep network for optical flow that matches all pairs of pixels once and refines a single flow field with a recurrent update operator.
Motion & Tracking
RAFT: Recurrent All-Pairs Field Transforms for Optical FlowThe ECCV 2020 best paper by Teed and Deng that replaced coarse-to-fine flow networks with an all-pairs correlation volume and a weight-tied recurrent update.
Motion & Tracking
Simple Online and Realtime TrackingThe 2016 paper by Bewley et al. that introduced SORT, showing that a Kalman filter and Hungarian matching on strong CNN detections rival far more complex online multi-object trackers.
Motion & Tracking
SORTSimple Online and Realtime Tracking, a multi-object tracker that links per-frame detections into tracks using a constant-velocity Kalman filter on each box and Hungarian matching on box overlap.