Siamese Learning Visual Tracking: A Survey <s> YCNN <s> In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision. <s> BIB001 </s> Siamese Learning Visual Tracking: A Survey <s> YCNN <s> We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm. <s> BIB002 </s> Siamese Learning Visual Tracking: A Survey <s> YCNN <s> Object tracking has been one of the most important and active research areas in the field of computer vision. A large number of tracking algorithms have been proposed in recent years with demonstrated success. However, the set of sequences used for evaluation is often not sufficient or is sometimes biased for certain types of algorithms. Many datasets do not have common ground-truth object positions or extents, and this makes comparisons among the reported quantitative results difficult. In addition, the initial conditions or parameters of the evaluated tracking algorithms are not the same, and thus, the quantitative results reported in literature are incomparable or sometimes contradictory. To address these issues, we carry out an extensive evaluation of the state-of-the-art online object-tracking algorithms with various evaluation criteria to understand how these methods perform within the same framework. In this work, we first construct a large dataset with ground-truth object positions and extents for tracking and introduce the sequence attributes for the performance analysis. Second, we integrate most of the publicly available trackers into one code library with uniform input and output formats to facilitate large-scale performance evaluation. Third, we extensively evaluate the performance of 31 algorithms on 100 sequences with different initialization settings. By analyzing the quantitative results, we identify effective approaches for robust tracking and provide potential future research directions in this field. <s> BIB003
[51] propose as possibly first but unpublished attempt two identical branches similar to VGGNet BIB001 with three conv and max-pooling layers, both linked to three fc layers. The conv layers share the same parameters. Each layer finishes with a ReLU except for the output which finishes with a sigmoid function. The network output is a 0-1 bounded prediction map with high values at pixels indicating object presence. The branches work as feature hierarchies aggregating fine-to-coarse spatial details, while the fc layers design spatiotemporal features as well as a general similarity function. Thus, YCNN learns discriminating features of growing complexity while simultaneously learning similarity between template and search region with corresponding prediction maps. Training is done in two stages on augmented images of objects from ImageNet and for fine-tuning with videos from VOT-15  and TB-100 BIB003 . Training minimises a weighted L 2 loss by using Adam BIB002 , mini-batches and dropout. Weighting is important as nearly 95 % of pixels in the prediction map have very low to zero values. During tracking, the feed forward pass infers then position as maximum in the prediction map. By averaging the prediction map over the five most confident maintained templates avoids drift. Repeating inference with scaled templates estimates additionally overall scale.