Siamese Learning Visual Tracking: A Survey <s> CFNet <s> A general non-parametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern recognition procedure: the mean shift. For discrete data, we prove the convergence of a recursive mean shift procedure to the nearest stationary point of the underlying density function and, thus, its utility in detecting the modes of the density. The relation of the mean shift procedure to the Nadaraya-Watson estimator from kernel regression and the robust M-estimators; of location is also established. Algorithms for two low-level vision tasks discontinuity-preserving smoothing and image segmentation - are described as applications. In these algorithms, the only user-set parameter is the resolution of the analysis, and either gray-level or color images are accepted as input. Extensive experimental results illustrate their excellent performance. <s> BIB001 </s> Siamese Learning Visual Tracking: A Survey <s> CFNet <s> Visual object tracking is a challenging computer vision problem with numerous real-world applications. This paper investigates the impact of convolutional features for the visual tracking problem. We propose to use activations from the convolutional layer of a CNN in discriminative correlation filter based tracking frameworks. These activations have several advantages compared to the standard deep features (fully connected layers). Firstly, they miti-gate the need of task specific fine-tuning. Secondly, they contain structural information crucial for the tracking problem. Lastly, these activations have low dimensionality. We perform comprehensive experiments on three benchmark datasets: OTB, ALOV300++ and the recently introduced VOT2015. Surprisingly, different to image classification, our results suggest that activations from the first layer provide superior tracking performance compared to the deeper layers. Our results further show that the convolutional features provide improved results compared to standard hand-crafted features. Finally, results comparable to state-of-the-art trackers are obtained on all three benchmark datasets. <s> BIB002 </s> Siamese Learning Visual Tracking: A Survey <s> CFNet <s> Machine learning techniques are often used in computer vision due to their ability to leverage large amounts of training data to improve performance. Unfortunately, most generic object trackers are still trained from scratch online and do not benefit from the large number of videos that are readily available for offline training. We propose a method for offline training of neural networks that can track novel objects at test-time at 100 fps. Our tracker is significantly faster than previous methods that use neural networks for tracking, which are typically very slow to run and not practical for real-time applications. Our tracker uses a simple feed-forward network with no online training required. The tracker learns a generic relationship between object motion and appearance and can be used to track novel objects that do not appear in the training set. We test our network on a standard tracking benchmark to demonstrate our tracker's state-of-the-art performance. Further, our performance improves as we add more videos to our offline training set. To the best of our knowledge, our tracker is the first neural-network tracker that learns to track generic objects at 100 fps. <s> BIB003
[54] adds a correlation filter and crop layers to the branch that concerns the template. These layers follow directly the convolutional network. The input is a larger region of the frame including the template, hence resolution of feature maps in the branches and prediction map is larger. Feature maps are further multiplied by a cosine window and cropped after correlation to remove the effect of circular boundaries. CFNet inherits the basic ability from SiamFC to discriminate spatial features with triplets of template region, search region and corresponding prediction map. Instead of unconstrained features, CFNet learns features that especially discriminate and solve the underlying ridge regression of the correlation layer by exploiting background samples in the surrounding region of the template. The learnt parameters of the correlation layer remain fixed during tracking, no online learning happens as shown by BIB002 . Training is done as with SiamFC by using the same algorithms on videos of objects from ImageNet. To make training end-to-end, emphasis has been on a differential correlation layer and on back-propagation of the parameters. Correlation is formulated in the Fourier space to preserve efficiency of computation. Tracking is similar simple as in SiamFC and computes position and scale by a single feed forward pass. The prediction map is multiplied by a spatial cosine window to penalise larger displacements. Instead of handling five different scale variations, scale is handled as by BIB001 . To fully exploit the correlation filter, the initial template is updated in each frame by a moving average. BIB003 proposes two convolutional branches inherited from AlexNet up to pool5. Both branches share the same parameters. These pool5 features of both branches are connected to a single vector and fed to three fc layers. ReLUs are used after each fc layer. The final fc layer links to an output layer with four nodes describing the bounding box. The output is scaled by a validated constant factor. GOTURN learns simultaneously the hierarchy of spatial features in the branches as well as spatiotemporal features and the similarity function in the fc layers to discriminate between template and search region with corresponding bounding boxes. Training is done in two stages on augmented images of objects from ImageNet and on videos from ALOV by using standard back-propagation of CaffeNet. Augmentation assumes linear translation and constant scale with parameters sampled from a Laplace distribution, hence small motion is assumed to occurs more frequently than larger displacements. Training minimises a L 1 loss between predicted and ground truth bounding box by using mini-batches, dropout and pre-training of the branches on ImageNet without fine-tuning these parameters to prevent overfitting. Tracking initialises the template in the first frame and updates the template with the predicted bounding box for each frame. Crops of the current and next frame yield template and search region. These crops are not exact but padded to add context.