A survey of datasets for visual tracking <s> The Kalal dataset <s> In this paper we propose a robust object tracking algorithm using a collaborative model. As the main challenge for object tracking is to account for drastic appearance change, we propose a robust appearance model that exploits both holistic templates and local representations. We develop a sparsity-based discriminative classifier (SD-C) and a sparsity-based generative model (SGM). In the S-DC module, we introduce an effective method to compute the confidence value that assigns more weights to the foreground than the background. In the SGM module, we propose a novel histogram-based method that takes the spatial information of each patch into consideration with an occlusion handing scheme. Furthermore, the update scheme considers both the latest observations and the original template, thereby enabling the tracker to deal with appearance change effectively and alleviate the drift problem. Numerous experiments on various challenging videos demonstrate that the proposed tracker performs favorably against several state-of-the-art algorithms. <s> BIB001 </s> A survey of datasets for visual tracking <s> The Kalal dataset <s> Firefly algorithm (FA) is a new meta-heuristic optimisation algorithm that mimics the social behaviour of fireflies flying in the tropical and temperate summer sky. In this study, a novel application of FA is presented as it is applied to solve tracking problem. A general optimisation-based tracking architecture is proposed and the parameters' sensitivity and adjustment of the FA in tracking system are studied. Experimental results show that the FA-based tracker can robustly track an arbitrary target in various challenging conditions. The authors compare the speed and accuracy of the FA with three typical tracking algorithms including the particle filter, meanshift and particle swarm optimisation. Comparative results show that the FA-based tracker outperforms the other three trackers. <s> BIB002 </s> A survey of datasets for visual tracking <s> The Kalal dataset <s> Adaptive tracking-by-detection methods have been widely studied with promising results. These methods first train a classifier in an online manner. Then, a sliding window is used to extract some samples from the local regions surrounding the former object location at the new frame. The classifier is then applied to these samples where the location of sample with maximum classifier score is the new object location. However, such classifier may be inaccurate when the training samples are imprecise which causes drift. Multiple instance learning (MIL) method is recently introduced into the tracking task, which can alleviate drift to some extent. However, the MIL tracker may detect the positive sample that is less important because it does not discriminatively consider the sample importance in its learning procedure. In this paper, we present a novel online weighted MIL (WMIL) tracker. The WMIL tracker integrates the sample importance into an efficient online learning procedure by assuming the most important sample (i.e., the tracking result in current frame) is known when training the classifier. A new bag probability function combining the weighted instance probability is proposed via which the sample importance is considered. Then, an efficient online approach is proposed to approximately maximize the bag likelihood function, leading to a more robust and much faster tracker. Experimental results on various benchmark video sequences demonstrate the superior performance of our algorithm to state-of-the-art tracking algorithms. <s> BIB003
Zdenek Kalal's dataset [23] , proposed in 2011, contains nine video sequences (see some examples in Fig. 11 ). Eight of the nine sequences were taken by cameras with strong motions (most of the time, cameras were embedded in vehicles): for example, a car chase is filmed from a helicopter in Sequence Car Chase, and some people are filmed in a parking lot from a flying engine in Pedestrian 3, 4 and 5 sequences. The stability of the camera is usually very poor, with jerky movements involved. This makes the global quality of the sequences low. Moreover, sometimes, the object to track can disappear from the scene. These sequences are very hard to work with due to the uncontrolled environment in which the objects are moving, their unpredictable movements, the cluttered backgrounds, and the illumination variations resulting from the outdoor conditions. This is the reason why only few algorithms have been validated on them  BIB001   BIB002 BIB003 . Note that ground truth annotations are provided for each sequence, as a text file containing the corners' coordinates of the bounding boxes of the object to track.