Template-matching methods for visual tracking have gained popularity recently due to their good performance and fast speed. However, they lack effective ways to adapt to changes in the target object's appearance, making their tracking accuracy still far from state-of-the-art. In this paper, we propose a dynamic memory network to adapt the template to the target's appearance variations during tracking. The reading and writing process of the external memory is controlled by an LSTM network with the search feature map as input. A spatial attention mechanism is applied to concentrate the LSTM input on the potential target as the location of the target is at first unknown. To prevent aggressive model adaptivity, we apply gated residual template learning to control the amount of retrieved memory that is used to combine with the initial template. In order to alleviate the drift problem, we also design a "negative" memory unit that stores templates for distractors, which are used to cancel out wrong responses from the object template. To further boost the tracking performance, an auxiliary classification loss is added after the feature extractor part. Unlike tracking-by-detection methods where the object's information is maintained by the weight parameters of neural networks, which requires expensive online fine-tuning to be adaptable, our tracker runs completely feed-forward and adapts to the target's appearance changes by updating the external memory. Moreover, the capacity of our model is not determined by the network size as with other trackers --- the capacity can be easily enlarged as the memory requirements of a task increase, which is favorable for memorizing long-term object information. Extensive experiments on the OTB and VOT datasets demonstrate that our trackers perform favorably against state-of-the-art tracking methods while retaining real-time speed.
Tracking-by-detection treats object tracking as a detection problem within an ROI image, where an online learned classifier is used to distinguish the target from the background. The difficulty of updating the classifier to adapt to appearance variations is that the bounding box predicted on each frame may not be accurate, which produces degraded training samples and thus gradually causes the tracker to drift. Numerous algorithms have been designed to mitigate the sample ambiguity caused by inaccurate predicted bounding boxes. @cite_50 formulates the online model learning process in a semi-supervised fashion by combining a given prior and the trained classifier. @cite_23 proposes a multiple instance learning scheme to solve the problem of inaccurate examples for online training. Instead of only focusing on facilitating the training process of the tracker, @cite_80 decomposes the tracking task into three parts---tracking, learning and detection, where a optical flow tracker is used for frame-to-frame tracking and an online trained detector is adopted to re-detect the target when drifting occurs.
@cite_80 @cite_23 @cite_50
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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. Tracking by detection based object tracking methods encounter numerous complications including object appearance changes, size and shape deformations, partial and full occlusions, which make online adaptation of classifiers and object models a substantial challenge. In this paper, we employ an object proposal network that generates a small yet refined set of bounding box candidates to mitigate the this object model refitting problem by concentrating on hard negatives when we update the classifier. This helps improving the discriminative power as hard negatives are likely to be due to background and other distractions. Another intuition is that, in each frame, applying the classifier only on the refined set of object-like candidates would be sufficient to eliminate most of the false positives. Incorporating an object proposal makes the tracker robust against shape deformations since they are handled naturally by the proposal stage. We demonstrate evaluations on the PETS 2016 dataset and compare with the state-of-theart trackers. Our method provides the superior results. Most tracking-by-detection algorithms train discriminative classifiers to separate target objects from their surrounding background. In this setting, noisy samples are likely to be included when they are not properly sampled, thereby causing visual drift. The multiple instance learning (MIL) paradigm has been recently applied to alleviate this problem. However, important prior information of instance labels and the most correct positive instance (i.e., the tracking result in the current frame) can be exploited using a novel formulation much simpler than an MIL approach. In this paper, we show that integrating such prior information into a supervised learning algorithm can handle visual drift more effectively and efficiently than the existing MIL tracker. We present an online discriminative feature selection algorithm that optimizes the objective function in the steepest ascent direction with respect to the positive samples while in the steepest descent direction with respect to the negative ones. Therefore, the trained classifier directly couples its score with the importance of samples, leading to a more robust and efficient tracker. Numerous experimental evaluations with state-of-the-art algorithms on challenging sequences demonstrate the merits of the proposed algorithm. In this paper, we address the problem of tracking an object in a video given its location in the first frame and no other information. Recently, a class of tracking techniques called “tracking by detection” has 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 degrade the classifier and can cause 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 propose a novel online MIL algorithm for object tracking that achieves superior results with real-time performance. We present thorough experimental results (both qualitative and quantitative) on a number of challenging video clips.