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The Visual Object Tracking challenge 2015, VOT2015, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 62 trackers are presented. The number of tested trackers makes VOT 2015 the largest benchmark on shortterm tracking to date. For each participating tracker, a short description(More)
Appearance modeling is very important for background modeling and object tracking. Subspace learning-based algorithms have been used to model the appearances of objects or scenes. Current vector subspace-based algorithms cannot effectively represent spatial correlations between pixel values. Current tensor subspace-based algorithms construct an offline(More)
Detection of moving vehicles in wide area motion imagery (WAMI) is increasingly important, with promising applications in surveillance, traffic scene understanding and public service applications such as emergency evacuation and policy security. However, the large camera motion, along with low contrast between vehicles and backgrounds, makes detection a(More)
Interactions between moving targets often provide discriminative clues for multiple target tracking (MTT), though many existing approaches ignore such interactions due to difficulty in effectively handling them. In this paper, we model interactions between neighbor targets by pair-wise motion context, and further encode such context into the global(More)
In this paper we formulate multi-target tracking (MTT) as a rank-1 tensor approximation problem and propose an &#x2113;<sub>1</sub> norm tensor power iteration solution. In particular, a high order tensor is constructed based on trajectories in the time window, with each tensor element as the affinity of the corresponding trajectory candidate. The local(More)
Tracking multiple vehicles in wide area traffic scenes is challenging due to high target density, severe similar target ambiguity, and low frame rate. In this paper, we propose a novel spatio-temporal context model, named maximum consistency context (MCC), to leverage the discriminative power and robustness in the scenario. For a candidate association, its(More)
Boosted by large and standardized benchmark datasets, visual object tracking has made great progress in recent years and brought about many new trackers. Among these trackers, correlation filter based tracking schema exhibits impressive robustness and accuracy. In this work, we present a fully functional correlation filter based tracking algorithm which is(More)
Due to its wide range of applications, matching between two graphs has been extensively studied and remains an active topic. By contrast, it is still under-exploited on how to jointly match multiple graphs, partly due to its intrinsic combinatorial intractability. In this work, we address this challenging problem in a principled way under the rank-1 tensor(More)
In recent studies of multi-target tracking, high-order association and its corresponding high-order affinity (or similarity) is often preferred over pairwise comparisons to capture high-order discriminative information. A naturally raised challenge is to calculate affinity (or similarity) among more than two target candidates. When target appearance is(More)
The appearance model is an important issue in the visual tracking community. Most subspace-based appearance models focus on the time correlation between the image observations of the object, but the spatial layout information of the object is ignored. This paper proposes a robust appearance model for visual tracking which effectively combines the spatial(More)