Siamese Learning Visual Tracking: A Survey <s> CONCLUSION <s> We consider the problem of decentralized detection under constraints on the number of bits that can be transmitted by each sensor. In contrast to most previous work, in which the joint distribution of sensor observations is assumed to be known, we address the problem when only a set of empirical samples is available. We propose a novel algorithm using the framework of empirical risk minimization and marginalized kernels, and analyze its computational and statistical properties both theoretically and empirically. We provide an efficient implementation of the algorithm, and demonstrate its performance on both simulated and real data sets. <s> BIB001 </s> Siamese Learning Visual Tracking: A Survey <s> CONCLUSION <s> Person re-identification is an important technique towards automatic search of a person's presence in a surveillance video. Two fundamental problems are critical for person re-identification, feature representation and metric learning. An effective feature representation should be robust to illumination and viewpoint changes, and a discriminant metric should be learned to match various person images. In this paper, we propose an effective feature representation called Local Maximal Occurrence (LOMO), and a subspace and metric learning method called Cross-view Quadratic Discriminant Analysis (XQDA). The LOMO feature analyzes the horizontal occurrence of local features, and maximizes the occurrence to make a stable representation against viewpoint changes. Besides, to handle illumination variations, we apply the Retinex transform and a scale invariant texture operator. To learn a discriminant metric, we propose to learn a discriminant low dimensional subspace by cross-view quadratic discriminant analysis, and simultaneously, a QDA metric is learned on the derived subspace. We also present a practical computation method for XQDA, as well as its regularization. Experiments on four challenging person re-identification databases, VIPeR, QMUL GRID, CUHK Campus, and CUHK03, show that the proposed method improves the state-of-the-art rank-1 identification rates by 2.2%, 4.88%, 28.91%, and 31.55% on the four databases, respectively. <s> BIB002 </s> Siamese Learning Visual Tracking: A Survey <s> CONCLUSION <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 </s> Siamese Learning Visual Tracking: A Survey <s> CONCLUSION <s> The Correlation Filter is an algorithm that trains a linear template to discriminate between images and their translations. It is well suited to object tracking because its formulation in the Fourier domain provides a fast solution, enabling the detector to be re-trained once per frame. Previous works that use the Correlation Filter, however, have adopted features that were either manually designed or trained for a different task. This work is the first to overcome this limitation by interpreting the Correlation Filter learner, which has a closed-form solution, as a differentiable layer in a deep neural network. This enables learning deep features that are tightly coupled to the Correlation Filter. Experiments illustrate that our method has the important practical benefit of allowing lightweight architectures to achieve state-of-the-art performance at high framerates. <s> BIB004
The various combinations of possible inputs, outputs and features and their implementation as layers in the network need definitely future research work. There are strong pros for a fixed similarity function, nevertheless learning similarity with fixed features or learning similarity and features jointly might conceal success as shown in the fields of re-identification BIB002 and sensor networks BIB001 . All methods keep for a good reason the tracking framework simple, namely to be able to better study the network's properties. There is much room for improvement concerning the tracking, for example by combining the network with filtering methods. Seeing the Siamese network as matching function or seeing the network as simplest recurrent network poses important questions about the integration of the network into the tracking framework which have not been answered yet. More training data is needed as well as new ideas for combining supervised and unsupervised training approaches as labelled data will always be limited. There is currently little knowledge about the influence of training loss on the overall performance. Insights into these topics by in-depth ablative analysis such as done by BIB003 , BIB004 are further needed. There are currently three lines of research: There is tracking research that assumes an initial label of the unknown object e.g. a bounding box and investigates tracking in the subsequent frames. These methods are combined with detection which on the one hand allows integrated perception but on the other hand the use of detectors unnecessarily restricts the tracker to certain object categories. The third line of research studies tracking jointly with attentional mechanism that does not assume any knowledge of the object.