A survey of datasets for visual tracking <s> The SPOT dataset <s> This paper presents a method of extracting discriminative stable regions (DSRs) from image, and applies them for object tracking. These DSRs obtained by using the criterion of maximal entropy and spatial discrimination present high appearance stability and strong spatial discriminative power, which enables them to tolerate more appearance variations and to effectively resist spatial distracters. Meanwhile, the adaptive fusion tracking incorporated k-means clustering can handle severe occlusion as well as disturbance of motion noise during target localization. In addition, an effective local update scheme is designed to adapt to the object change for ensuring the tracking robustness. Experiments are carried out on several challenging sequences and results show that our method performs well in terms of object tracking, even in the presence of occlusion, deformation, illumination change, moving camera and spatial distracter. <s> BIB001 </s> A survey of datasets for visual tracking <s> The SPOT dataset <s> Model-free trackers can track arbitrary objects based on a single (bounding-box) annotation of the object. Whilst the performance of model-free trackers has recently improved significantly, simultaneously tracking multiple objects with similar appearance remains very hard. In this paper, we propose a new multi-object model-free tracker (based on tracking-by-detection) that resolves this problem by incorporating spatial constraints between the objects. The spatial constraints are learned along with the object detectors using an online structured SVM algorithm. The experimental evaluation of our structure-preserving object tracker (SPOT) reveals significant performance improvements in multi-object tracking. We also show that SPOT can improve the performance of single-object trackers by simultaneously tracking different parts of the object. <s> BIB002 </s> A survey of datasets for visual tracking <s> The SPOT dataset <s> The core component of most modern trackers is a discriminative classifier, tasked with distinguishing between the target and the surrounding environment. To cope with natural image changes, this classifier is typically trained with translated and scaled sample patches. Such sets of samples are riddled with redundancies—any overlapping pixels are constrained to be the same. Based on this simple observation, we propose an analytic model for datasets of thousands of translated patches. By showing that the resulting data matrix is circulant, we can diagonalize it with the discrete Fourier transform, reducing both storage and computation by several orders of magnitude. Interestingly, for linear regression our formulation is equivalent to a correlation filter, used by some of the fastest competitive trackers. For kernel regression, however, we derive a new kernelized correlation filter (KCF), that unlike other kernel algorithms has the exact same complexity as its linear counterpart. Building on it, we also propose a fast multi-channel extension of linear correlation filters, via a linear kernel, which we call dual correlation filter (DCF). Both KCF and DCF outperform top-ranking trackers such as Struck or TLD on a 50 videos benchmark, despite running at hundreds of frames-per-second, and being implemented in a few lines of code (Algorithm 1). To encourage further developments, our tracking framework was made open-source. <s> BIB003
The movements of the objects in a same sequence are related to each other. For example, in the Red Flowers sequence, a bunch of tulips are moving due to the wind, and in the Skydiving sequence, six people are doing synchronized acrobatics in the sky. The appearance of the tracked objects can also strongly vary with time (see the Hunting sequence). For all the sequences, a ground truth annotation BIB003 The Zhang dataset [47] . Three video sequences with high deformations and motions of the objects to track file (Matlab matrices format) is provided, containing the center and size of the bounding box of each tracked object. For example, for the Airshow sequence, four planes have been manually annotated, and three people for the Parade sequence. Because of its novelty, only one work uses this dataset and proposes to exploit structural relations between the objects during visual tracking BIB001 BIB002 .