High Confidence Updating Strategy on Staple Trackers

Zou, Qian; Lin, Shaofu; Du, Yanan

doi:10.1109/irce.2018.8492925

Cited by 1 publication

(2 citation statements)

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“…When the PAR and R max are both greater than a predefined threshold, the result is judged as reliable, thus the correlation filter model in [18] is updated. Similarly, APCE is defined in [19], as…”

Section: Update Using Response Mapsmentioning

confidence: 99%

“…After the target in frame n is tracked, the CFs in the last r frames, including #n, are firstly added into the CF set while those in other n-r frames are clustered into K classes; afterwards, the CF with the lowest classification error in each cluster is added into the CF set. The K + r CFs are combined with different weights to form the final strong CF, which can be formulated as in (19), and ρ i n is the weight of the i-th filter in frame #n calculated as in (20), where e i denotes the training error of the filter calculated as in (21), in which (x t , y t ) is the new training sample of the t-th frame, whose spatial size is…”

Section: Update Strategy Based On Correlation Filtermentioning

confidence: 99%

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Model Update Strategies about Object Tracking: A State of the Art Review

et al. 2019

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Object tracking has always been an interesting and essential research topic in the domain of computer vision, of which the model update mechanism is an essential work, therefore the robustness of it has become a crucial factor influencing the quality of tracking of a sequence. This review analyses on recent tracking model update strategies, where target model update occasion is first discussed, then we give a detailed discussion on update strategies of the target model based on the mainstream tracking frameworks, and the background update frameworks are discussed afterwards. The experimental performances of the trackers in recent researches acting on specific sequences are listed in this review, where the superiority and some failure cases on each of them are discussed, and conclusions based on those performances are then drawn. It is a crucial point that design of a proper background model as well as its update strategy ought to be put into consideration. A cascade update of the template corresponding to each deep network layer based on the contributions of them to the target recognition can also help with more accurate target location, where target saliency information can be utilized as a tool for state estimation.Diverse variations regarding the target usually occur in the process of tracking, i.e., variations arise from changes of the outside environment, such as view angle, camera orientation, environmental illumination, etc., and inherent changes of the object, such as self-rotation, self-deformation, and self-variation of target appearance; therefore, a tracker with more robust capacity has to be designed, whose framework structure and sample learning strategy are of key importance, which guarantees its real-time and accuracy. Consequently, researching an update strategy with higher robustness and efficiency has been of greater essence.Object tracking framework can be usually typed into two categories: generative frameworks and discriminative ones, where, for the former framework, i.e., particle filter, sparse coding, linear predictions [5,6], Kalman filter, etc., target and background models are established at the beginning and the features of them are extracted for the search of similar target or background features in succeeding frame images to iteratively locate the target; The latter, i.e., deep neural networks, correlation filter, random forest, feature bagging [7], etc., gets the object location by drawing candidate target patches within a region and then select one that is distinguished from given background patches. With the progress of researches on machine learning and deep learning tracking frameworks, the model update has become a widely concerned part in recent researches. A good update mechanism is a crucial respect measuring the reliability of a tracker. On the one hand, template models of the target and background should be constantly updated to catch up with the their variation, which is a fundamental requirement of model adaptation. On the other hand, the parameter model must be adjuste...

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Section: Update Using Response Mapsmentioning

confidence: 99%

Section: Update Strategy Based On Correlation Filtermentioning

confidence: 99%