Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   demonstrate that the support vector tracking (SVT) framework first proposed by Avidan is equivalent to the canonical Lucas-Kanade (LK) algorithm with a ighted Euclidean norm. From this equivalence  empirically demonstrate that in many circumstances the canonical SVT approach is unstable, and characterize these circumstances theoretically.  then propose a novel ldquononpositive support kernel machinerdquo (NSKM) to circumvent this limitation and allow the effective use of discriminative classification within the ighted LK framework. This approach ensures that the pseudo-Hessian realized within the ighted LK algorithm is positive semidefinite which allows for fast convergence and accurate alignment/tracking. A further benefit of our proposed method is that the NSKM solution results in a much sparser kernel machine than the canonical SVM leading to sizeable computational savings and much improved alignment performance.
['Simon Lucey']
I am writing a research paper on a idea Enforcing non-positive weights for stable support vector tracking
[{"from": "human", "value": "I am writing a research paper on a idea Enforcing non-positive weights for stable support vector tracking"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   demonstrate that the support vector tracking (SVT) framework first proposed by Avidan is equivalent to the canonical Lucas-Kanade (LK) algorithm with a ighted Euclidean norm. From this equivalence  empirically demonstrate that in many circumstances the canonical SVT approach is unstable, and characterize these circumstances theoretically.  then propose a novel ldquononpositive support kernel machinerdquo (NSKM) to circumvent this limitation and allow the effective use of discriminative classification within the ighted LK framework. This approach ensures that the pseudo-Hessian realized within the ighted LK algorithm is positive semidefinite which allows for fast convergence and accurate alignment/tracking. A further benefit of our proposed method is that the NSKM solution results in a much sparser kernel machine than the canonical SVM leading to sizeable computational savings and much improved alignment performance."}]