A survey of datasets for visual tracking <s> The BoBoT dataset <s> Recently, several image gradient and edge based features have been introduced. In unison, they all discovered that object shape is a strong cue for recognition and tracking. Generally their basic feature extraction relies on pixel-wise gradient or edge computation using discrete filter masks, while scale invariance is later achieved by higher level operations like accumulating histograms or abstracting edgels to line segments. In this paper we show a novel and fast way to compute region based gradient features which are scale invariant themselves. We developed specialized, quick learnable weak classifiers that are integrated into our adaptively boosted observation model for particle filter based tracking. With an ensemble of region based gradient features this observation model is able to reliably capture the shape of the tracked object. The observation model is adapted to new object and background appearances while tracking. Thus we developed advanced methods to decide when to update the model, or in other words, if the filter is on target or not. We evaluated our approach using the BoBoT1 as well as the PROST2 datasets. <s> BIB001 </s> A survey of datasets for visual tracking <s> The BoBoT dataset <s> Visual tracking is still an open problem because one needs to discriminate between the target object and background ::: under long duration. There is a major problem with conventional adaptive tracking where the target object is incorrectly ::: learnt (adapted) during runtime, resulting in poor performance of tracker. In this paper, we address this problem by ::: proposing validation-update strategy to minimize the error of false patches updating. The classifier we use is based on ::: boosted ensemble of Local Dominant Orientation (LDO). However, since LDO features contain binary values which are ::: unsuitable for classification, we have added a process to the online boosting learning algorithm that permits the two ::: binary values of "0" and "1". We elevate the tracker performance by pairing the classifier with normalized crosscorrelation ::: of patches tracked by Lukas-Kanade tracker. In the experiment conducted, we compare our method with two ::: other state-of-the-art adaptive trackers using BoBot dataset. Our method yields good tracking performance under variety ::: of scenarios set by BoBot dataset. <s> BIB002
The Bonn Benchmark on Tracking (BoBoT)  frames have a size of 320×240 pixels and their numbers vary from 305 to 1308. Ground truth annotations are also given for each sequence. They correspond to the coordinates of the target object's bounding box and its size (in text format files). A java tool evaluator enables to compare different algorithms in terms of tracking errors, with respect to a specific scheme. As shown in Fig. 4 , each sequence (named from A to L) is dedicated to highlight specific performances of the algorithms (for example, tracking specific motions such as translations, rotations, oscillations, or tracking under occlusion, appearance and disappearance of the object, illumination changes, etc.). This dataset was used in several recent works, such as BIB001    BIB002 .