Matches in SemOpenAlex for { <https://semopenalex.org/work/W3203399982> ?p ?o ?g. }
- W3203399982 endingPage "102098" @default.
- W3203399982 startingPage "102098" @default.
- W3203399982 abstract "With the introduction of correlation filtering (CF), the performance of visual object tracking is significantly improved. Circular shifts collecting samples is a key component of the CF tracker, and it also causes negative boundary effects. Most trackers add spatial regularization to alleviate boundary effects well. However, these trackers ignore the effect of environmental changes on tracking performance, and the filter discriminates poorly in the background interference. Here, to break these limitations, we propose a new correlation filter model, namely Environmental Perception with Spatial Regularization Correlation Filter for Visual Tracking. Specifically, we use the Average Peak to Correlation Energy (APCE) and the response value error between the two frames together to perceive environmental changes, which adjusts the learning rate to make the template more adaptable to environmental changes. To enhance the discriminatory capability of the filter, we use real background information as negative samples to train the filter model. In addition, the introduction of the regular term destroys the closed solution of CF, and this problem can be effectively solved by the use of the alternating direction method of multipliers (ADMM). Extensive experimental evaluations on three large tracking benchmarks are performed, which demonstrate the good performance of the proposed method over some of the state-of-the-art trackers. • We propose a new framework for visual target tracking that perceives changes in the environment and adaptively adjusts the learning rate to avoid template pollution. Specifically, the APEC indicator and the response error between two frames are used to jointly perceive changes in the environment and adaptively adjust the learning rate. • We uses realistic background information to train filters, which not only suppresses the interference of background information, but also enhances the discrimination ability of the filter. • We solve the energy function of the filter as a non-closed solution. To reduce computational complexity, we use ADMM algorithm to transform the energy function into two subproblems with globally optimal solutions, because both subproblems are convex. Therefore, the two sub-problems have their own closed solution and globally optimal solutions. • Experimental results on three benchmarks, i.e., OTB-2015, TC-128 and VOT-2018 validate that the proposed approach obviously improves the tracking accuracy of CF based trackers." @default.
- W3203399982 created "2021-10-11" @default.
- W3203399982 creator A5004593122 @default.
- W3203399982 creator A5026666435 @default.
- W3203399982 creator A5030379568 @default.
- W3203399982 creator A5046214153 @default.
- W3203399982 creator A5058264767 @default.
- W3203399982 date "2021-12-01" @default.
- W3203399982 modified "2023-09-24" @default.
- W3203399982 title "Environmental Perception with Spatial Regularization Correlation Filter for Visual Tracking" @default.
- W3203399982 cites W1892578678 @default.
- W3203399982 cites W1915785815 @default.
- W3203399982 cites W1955514522 @default.
- W3203399982 cites W1964846093 @default.
- W3203399982 cites W2018863717 @default.
- W3203399982 cites W2044986361 @default.
- W3203399982 cites W2154889144 @default.
- W3203399982 cites W2158592639 @default.
- W3203399982 cites W2162349892 @default.
- W3203399982 cites W2214352687 @default.
- W3203399982 cites W2519007024 @default.
- W3203399982 cites W2520477759 @default.
- W3203399982 cites W2557641257 @default.
- W3203399982 cites W2558899534 @default.
- W3203399982 cites W2584665002 @default.
- W3203399982 cites W2599547527 @default.
- W3203399982 cites W2768634781 @default.
- W3203399982 cites W2955983623 @default.
- W3203399982 cites W2962864296 @default.
- W3203399982 cites W2964069521 @default.
- W3203399982 cites W2968127870 @default.
- W3203399982 cites W2987346479 @default.
- W3203399982 cites W3035466700 @default.
- W3203399982 cites W3087520358 @default.
- W3203399982 cites W3092266750 @default.
- W3203399982 cites W3102624093 @default.
- W3203399982 cites W4292363360 @default.
- W3203399982 doi "https://doi.org/10.1016/j.displa.2021.102098" @default.
- W3203399982 hasPublicationYear "2021" @default.
- W3203399982 type Work @default.
- W3203399982 sameAs 3203399982 @default.
- W3203399982 citedByCount "0" @default.
- W3203399982 crossrefType "journal-article" @default.
- W3203399982 hasAuthorship W3203399982A5004593122 @default.
- W3203399982 hasAuthorship W3203399982A5026666435 @default.
- W3203399982 hasAuthorship W3203399982A5030379568 @default.
- W3203399982 hasAuthorship W3203399982A5046214153 @default.
- W3203399982 hasAuthorship W3203399982A5058264767 @default.
- W3203399982 hasConcept C106131492 @default.
- W3203399982 hasConcept C117220453 @default.
- W3203399982 hasConcept C154945302 @default.
- W3203399982 hasConcept C15744967 @default.
- W3203399982 hasConcept C169760540 @default.
- W3203399982 hasConcept C19417346 @default.
- W3203399982 hasConcept C202474056 @default.
- W3203399982 hasConcept C2524010 @default.
- W3203399982 hasConcept C26760741 @default.
- W3203399982 hasConcept C2775936607 @default.
- W3203399982 hasConcept C2776135515 @default.
- W3203399982 hasConcept C2781238097 @default.
- W3203399982 hasConcept C31972630 @default.
- W3203399982 hasConcept C33923547 @default.
- W3203399982 hasConcept C41008148 @default.
- W3203399982 hasConcept C56461940 @default.
- W3203399982 hasConcept C57501372 @default.
- W3203399982 hasConcept C86803240 @default.
- W3203399982 hasConceptScore W3203399982C106131492 @default.
- W3203399982 hasConceptScore W3203399982C117220453 @default.
- W3203399982 hasConceptScore W3203399982C154945302 @default.
- W3203399982 hasConceptScore W3203399982C15744967 @default.
- W3203399982 hasConceptScore W3203399982C169760540 @default.
- W3203399982 hasConceptScore W3203399982C19417346 @default.
- W3203399982 hasConceptScore W3203399982C202474056 @default.
- W3203399982 hasConceptScore W3203399982C2524010 @default.
- W3203399982 hasConceptScore W3203399982C26760741 @default.
- W3203399982 hasConceptScore W3203399982C2775936607 @default.
- W3203399982 hasConceptScore W3203399982C2776135515 @default.
- W3203399982 hasConceptScore W3203399982C2781238097 @default.
- W3203399982 hasConceptScore W3203399982C31972630 @default.
- W3203399982 hasConceptScore W3203399982C33923547 @default.
- W3203399982 hasConceptScore W3203399982C41008148 @default.
- W3203399982 hasConceptScore W3203399982C56461940 @default.
- W3203399982 hasConceptScore W3203399982C57501372 @default.
- W3203399982 hasConceptScore W3203399982C86803240 @default.
- W3203399982 hasFunder F4320321001 @default.
- W3203399982 hasFunder F4320328898 @default.
- W3203399982 hasFunder F4320335787 @default.
- W3203399982 hasLocation W32033999821 @default.
- W3203399982 hasOpenAccess W3203399982 @default.
- W3203399982 hasPrimaryLocation W32033999821 @default.
- W3203399982 hasRelatedWork W2013820100 @default.
- W3203399982 hasRelatedWork W2086383524 @default.
- W3203399982 hasRelatedWork W2106981188 @default.
- W3203399982 hasRelatedWork W2274330372 @default.
- W3203399982 hasRelatedWork W2396101943 @default.
- W3203399982 hasRelatedWork W2955100824 @default.
- W3203399982 hasRelatedWork W3036550512 @default.
- W3203399982 hasRelatedWork W3126660769 @default.