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Recent attempts of integrating metric learning in visual tracking have produced encouraging results. Instead of using fixed and pre-specified metric in visual appearance matching, these methods are able to learn and adjust the metric adaptively by finding the best projection of the feature space. Such learned metric is by design the best to discriminate the target of interest and its distracters from...
STK software is usually used to calculate access time windows for observing spot targets in aerospace, but it's difficult to develop some applications based on STK. In this paper, we present a novel algorithm for calculating roll angles and access time windows of spot targets based on space geometry model. The algorithm can quickly check the visibility of spot targets, and get roll angles and access...
Most affinity-based grouping methods only model the inclusive relation among the data. When the data set contains a significant amount of noise data that should not be included in any clusters, these methods are likely to lead to undesired results. To address this issue, this paper presents a new approach called bipolar grouping that is targeted on extracting the groups from the data while excluding...
Matching based on local brightness is quite limited, because small changes on local appearance invalidate the constancy in brightness. The root of this limitation is its treatment regardless of the information from the spatial contexts. This papers leaps from brightness constancy to context constancy, and thus from optical flow to contextual flow. It presents a new approach that incorporates contexts...
The changes of the target's visual appearance often lead to tracking failure in practice. Hence, trackers need to be adaptive to non-stationary appearances to achieve robust visual tracking. However, the risk of adaptation drift is common in most existing adaptation schemes. This paper describes a bi-subspace model that stipulates the interactions of two different visual cues. The visual appearance...
This paper presents a novel distributed framework for multi-target tracking with an efficient data association computation. A decentralized representation of trackerspsila motion and association variables is adopted. Considering the interleaved nature of data association and tracker filtering, the multi-target tracking is formulated as a missing data problem, and the solution is found by the proposed...
The observation models in tracking algorithms are critical to both tracking performance and applicable scenarios but are often simplified to focus on fixed level of certain target properties such as appearances and structures. In this paper, we propose a unified tracking paradigm in which targets are represented by Markov random fields of interest regions and introduce a new way to adapt observation...
Video-based multiple target tracking (MTT) is a challenging task when similar targets are present in close vicinity. Because their visual observations are mixed and difficult to segment, their motions have to be estimated jointly. Most existing approaches perform this joint motion estimation in a centralized fashion and involve searching a rather high dimensional space, and thus leading to quite complicated...
Long-duration tracking of general targets is quite challenging for computer vision, because in practice target may undergo large uncertainties in its visual appearance and the unconstrained environments may be cluttered and distractive, although tracking has never been a challenge to the human visual system. Psychological and cognitive findings indicate that the human perception is attentional and...
Many emerging applications require tracking targets in video. Most existing visual tracking methods do not work well when the target is motion-blurred (especially due to fast motion), because the imperfectness of the target's appearances invalidates the image matching model (or the measurement model) in tracking. This paper presents a novel method to track motion-blurred targets by taking advantage...
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