A survey of datasets for visual tracking <s> The Birchfield dataset <s> An algorithm for tracking a person's head is presented. The head's projection onto the image plane is modeled as an ellipse whose position and size are continually updated by a local search combining the output of a module concentrating on the intensity gradient around the ellipse's perimeter with that of another module focusing on the color histogram of the ellipse's interior. Since these two modules have roughly orthogonal failure modes, they serve to complement one another. The result is a robust, real-time system that is able to track a person's head with enough accuracy to automatically control the camera's pan, tilt, and zoom in order to keep the person centered in the field of view at a desired size. Extensive experimentation shows the algorithm's robustness with respect to full 360-degree out-of-plane rotation, up to 90-degree tilting, severe but brief occlusion, arbitrary camera movement, and multiple moving people in the background. <s> BIB001 </s> A survey of datasets for visual tracking <s> The Birchfield dataset <s> In this paper, we address the problem of learning an adaptive appearance model for object tracking. In particular, a class of tracking techniques called “tracking by detection” have been shown to give promising results at real-time speeds. These methods train a discriminative classifier in an online manner to separate the object from the background. This classifier bootstraps itself by using the current tracker state to extract positive and negative examples from the current frame. Slight inaccuracies in the tracker can therefore lead to incorrectly labeled training examples, which degrades the classifier and can cause further drift. In this paper we show that using Multiple Instance Learning (MIL) instead of traditional supervised learning avoids these problems, and can therefore lead to a more robust tracker with fewer parameter tweaks. We present a novel online MIL algorithm for object tracking that achieves superior results with real-time performance. <s> BIB002 </s> A survey of datasets for visual tracking <s> The Birchfield dataset <s> Recently sparse representation has been applied to visual tracker by modeling the target appearance using a sparse approximation over a template set, which leads to the so-called L1 trackers as it needs to solve an l 1 norm related minimization problem for many times. While these L1 trackers showed impressive tracking accuracies, they are very computationally demanding and the speed bottleneck is the solver to l 1 norm minimizations. This paper aims at developing an L1 tracker that not only runs in real time but also enjoys better robustness than other L1 trackers. In our proposed L1 tracker, a new l 1 norm related minimization model is proposed to improve the tracking accuracy by adding an l 1 norm regularization on the coefficients associated with the trivial templates. Moreover, based on the accelerated proximal gradient approach, a very fast numerical solver is developed to solve the resulting l 1 norm related minimization problem with guaranteed quadratic convergence. The great running time efficiency and tracking accuracy of the proposed tracker is validated with a comprehensive evaluation involving eight challenging sequences and five alternative state-of-the-art trackers. <s> BIB003 </s> A survey of datasets for visual tracking <s> The Birchfield dataset <s> This paper made major research on the target representation problem, which plays a significant role in visual tracking, but has received little attention in most researches. In order to fulfill the requirements of tracking robustness and effectiveness in practical conditions, a dynamic appearance model is constructed. Due to particle filter's excellent characteristics, it is employed in this paper not only to estimate target's state, but also to construct the dynamic observation model integrated by multiple cues. In the proposed method, a dynamic multi-cue integration model is constructed for particle filter framework. And a systematic study is done on evaluating cue's weight. Specially, a particle filter based weight tracker is designed to update multi-cue's integrating manner online, so as to adapt the observation model to target's appearance changes. In such a way, a double-particle-filter based tracking framework is formed, and it is field tested on a variety of videos in different tracking conditions. In the experiments and comparisons, the applicable conditions of the proposed dynamic model are discussed, and its robustness and effectiveness are demonstrated. <s> BIB004 </s> A survey of datasets for visual tracking <s> The Birchfield dataset <s> In this paper, we propose a biologically inspired framework of visual tracking based on proto-objects. Given an image sequence, proto-objects are first detected by combining saliency map and topic model. Then the target is tracked based on spatial and saliency information of the proto-objects. In the proposed Bayesian approach, states of the target and proto-objects are jointly estimated over time. Gibbs sampling has been used to optimize the estimation during the tracking process. The proposed method robustly handles occlusion, distraction, and illumination change in the experiments. Experimental results also demonstrate that the proposed method outperforms the state-of-the-art methods in challenging tracking tasks. <s> BIB005 </s> A survey of datasets for visual tracking <s> The Birchfield 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> BIB006 </s> A survey of datasets for visual tracking <s> The Birchfield dataset <s> Locally Orderless Tracking (LOT) is a visual tracking algorithm that automatically estimates the amount of local (dis)order in the target. This lets the tracker specialize in both rigid and deformable objects on-line and with no prior assumptions. We provide a probabilistic model of the target variations over time. We then rigorously show that this model is a special case of the Earth Mover's Distance optimization problem where the ground distance is governed by some underlying noise model. This noise model has several parameters that control the cost of moving pixels and changing their color. We develop two such noise models and demonstrate how their parameters can be estimated on-line during tracking to account for the amount of local (dis)order in the target. We also discuss the significance of this on-line parameter update and demonstrate its contribution to the performance. Finally we show LOT's tracking capabilities on challenging video sequences, both commonly used and new, displaying performance comparable to state-of-the-art methods. <s> BIB007
The Birchfield dataset [10] is probably the first dataset that was made publicly available, in 1998, for visual tracking. The goal was to test an elliptical head visual tracker BIB001 under varying conditions (appearance changes, occlusions) or motions (rotations of objects, zoom of camera, etc.). Seventeen color sequences are provided. They show different people (from one to four) moving, occluding, showing their face, but also their profile, or the back of their head. Moreover, due to file compression, the quality of these video sequences is very low, which makes them very challenging. A ground truth text file is also provided, containing bounding ellipses of tracked objects. Lots of works were validated on these sequences. This is for instance the case of the algorithms proposed in  BIB007 BIB002 BIB003 BIB005 BIB004 BIB006 . Especially, sequence Seq_mb (see Fig. 2 ) is one of the most used.