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Intro
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Tracking: problem & data Online tracking: following the location of one target in video starting from a bounding box in the first frame by computationally exploiting the temporal dimension .
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Amsterdam Library Of Videos ALOV contains more than 300 videos, mostly with one event per video, annotated the ground truth/Sth frame.
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NCC Normalized Cross-Correlation Direct target matching by normalized cross-correlation Intensity values in the initial target box as template. Sampling uniformly around the previous position
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LKT Lucas Kanade Tracker Affine-transformed match between the target and candidates. Around the previous location by incremental image alignment. Spatiotemporal derivatives Warping around the previou…
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MST Mean Shift Tracker Matching with RGB-histogramDy the Bhattacharyya metric The location by mean shift maximizes the Bhattacharyya modus. Target histogram formed in the first frame No update
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IVT Incremental Visual Tracking Extended appearance model captures the past i Figen Images Stored in a leaking memory. Similarity by subspace distance. Gaussian particle filtering sampling around the…
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TST Tracking by Sampling Trackers Sampling many IT-like trackers as an extended model The state stores the center, scale and spatial Multiple locations and scales, filters, motion in multiple Gaussia…
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The tracker trains a linear discriminant classifier as only the difference between the object and the background counts. SURF texture features from target against the local background. Updated by a l…
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Updating the FBT-model The foreground template is updated in every frame as the weighted average between the old template and the object feature in the current frame
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HBT Hough-Based Tracking Discriminative classifier on Lab-color, gradients and positions The Hough Forest provides a probability map of the target area. Back projection aims to avoid inclusion of the…
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TLD Tracking, Learning and Detection 50 Top detections on LBPs and KLT optical flow are combined by NCC. Samples are selected in, around and away from the target to update. If neither of the two trac…
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Shadows The effect of shadows. Heavy shadow has an impact almost for all.
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Systematic overview of trackers Trackers have to find an integral solution on the basis of 1. a spotial representation, 2. an appearance representation, 3. a motion model 4. an inference method to ge…
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Feature types Essentially different types of features: 1. Geometric 2D modeloverall locality matters 2. Histogram 10 array-spatially disturbed, sufficient identification 3. Feature OD vector = featur…
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Updating revisited 1. No updating does remarkably well do not over-update. 2. Update at least for scale, rotation and occlusion 3. Focusing on one condition ruins another broad dataset
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The hardness of tracking Tracking aims to learn a target from the first few pictures; the target and the background may be dynamic in appearance, with unpredicted motion, and in difficult scenes.
Description:
Explore object tracking in computer vision through this comprehensive lecture by Arnold W.M. Smeulders from the University of Amsterdam. Delve into the challenges and recent developments in tracking algorithms, including insights from an international experimental review. Learn about various tracking methods such as Normalized Cross-Correlation, Lucas Kanade Tracker, Mean Shift Tracker, and Incremental Visual Tracking. Examine the impact of shadows, feature types, and updating strategies on tracking performance. Gain a systematic overview of trackers, understanding their spatial representation, appearance representation, motion models, inference methods, and internal model updates. Analyze the hardness of tracking and the complexities involved in learning targets from dynamic scenes with unpredicted motion.

Object Tracking

University of Central Florida
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#Computer Science #Artificial Intelligence #Computer Vision #Object Detection #Object Tracking
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Intro