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Object Tracking via Dynamic Feature Selection Processes

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Giorgio Roffo, Simone Melzi

Computer Vision and Pattern Recognition · Submitted 7 Sep 2016

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim DFST provides a significant gain in accuracy and precision allowing the use of a dynamic set of features that results in an increased system flexibility.

how it is stated · asserted 1.00 read the record →

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arXiv ID1609.01958 (version 1)
Pith IDOLATHIVA, Pith's permanent record of this paper and its review
CitationGiorgio Roffo, Simone Melzi. "Object Tracking via Dynamic Feature Selection Processes." arXiv:1609.01958 (2016). https://pith.science/paper/OLATHIVA
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 5,482 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.06 · human participants 0.03 · controlled comparison 0.23 · randomized assignment 0.01 · states quantitative result 0.05 · compares with prior results 0.43 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

DFST proposes an optimized visual tracking algorithm based on the real-time selection of locally and temporally discriminative features. A feature selection mechanism is embedded in the Adaptive colour Names (CN) tracking system that adaptively selects the top-ranked discriminative features for tracking. DFST provides a significant gain in accuracy and precision allowing the use of a dynamic set of features that results in an increased system flexibility. DFST is based on the unsupervised method "Infinite Feature Selection" (Inf-FS), which ranks features according with their "redundancy" without using class labels. By using a fast online algorithm for learning dictionaries the size of the box is adapted during the processing. At each update, we use multiple examples around the target (at different positions and scales). DFST also improved the CN by adding micro-shift at the predicted position and bounding box adaptation.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 DFST proposes an optimized visual tracking algorithm based on the real-time selection of locally and temporally discriminative features. contribution 0.97 0.79 0.22 asserted 1.00
s1 A feature selection mechanism is embedded in the Adaptive colour Names (CN) tracking system that adaptively selects the top-ranked discriminative features for tracking. method 0.59 0.40 0.07 asserted 1.00
s2 DFST provides a significant gain in accuracy and precision allowing the use of a dynamic set of features that results in an increased system flexibility. result 0.89 0.80 0.65 asserted 1.00
s3 DFST is based on the unsupervised method "Infinite Feature Selection" (Inf-FS), which ranks features according with their "redundancy" without using class labels. method 0.99 0.41 0.06 asserted 1.00
s4 By using a fast online algorithm for learning dictionaries the size of the box is adapted during the processing. method 0.98 0.53 0.04 asserted 1.00
s5 At each update, we use multiple examples around the target (at different positions and scales). method 0.99 0.74 0.03 asserted 0.97
s6 DFST also improved the CN by adding micro-shift at the predicted position and bounding box adaptation. contribution 0.52 0.79 0.10 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.59.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

DFST proposes an optimized visual tracking algorithm based on the real-time selection of locally and temporally discriminative features. A feature selection mechanism is embedded in the Adaptive colour Names (CN) tracking system that adaptively selects the top-ranked discriminative features for tracking. DFST provides a significant gain in accuracy and precision allowing the use of a dynamic set of features that results in an increased system flexibility. DFST is based on the unsupervised method "Infinite Feature Selection" (Inf-FS), which ranks features according with their "redundancy" without using class labels. By using a fast online algorithm for learning dictionaries the size of the box is adapted during the processing. At each update, we use multiple examples around the target (at different positions and scales). DFST also improved the CN by adding micro-shift at the predicted position and bounding box adaptation.

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