Siamese Learning Visual Tracking: A Survey <s> SINT <s> We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overriding in the fully-connected layers we employed a recently-developed regularization method called "dropout" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry. <s> BIB001 </s> Siamese Learning Visual Tracking: A Survey <s> SINT <s> There is a large variety of trackers, which have been proposed in the literature during the last two decades with some mixed success. Object tracking in realistic scenarios is a difficult problem, therefore, it remains a most active area of research in computer vision. A good tracker should perform well in a large number of videos involving illumination changes, occlusion, clutter, camera motion, low contrast, specularities, and at least six more aspects. However, the performance of proposed trackers have been evaluated typically on less than ten videos, or on the special purpose datasets. In this paper, we aim to evaluate trackers systematically and experimentally on 315 video fragments covering above aspects. We selected a set of nineteen trackers to include a wide variety of algorithms often cited in literature, supplemented with trackers appearing in 2010 and 2011 for which the code was publicly available. We demonstrate that trackers can be evaluated objectively by survival curves, Kaplan Meier statistics, and Grubs testing. We find that in the evaluation practice the F-score is as effective as the object tracking accuracy (OTA) score. The analysis under a large variety of circumstances provides objective insight into the strengths and weaknesses of trackers. <s> BIB002
[13] propose two identical query and search networks inherited from AlexNet BIB001 and VGGNet with five conv and two max-pooling layers, three region pooling layers, an fc layer and a final L 2 normalisation layer. ReLUs follow each conv layer. Max-pooling is done after the first two conv layers. Both networks are unconnected but share the same weights. Instead of object and search region templates, the whole two subsequent frames are input, hence bounding boxes locating the object in the query frame and bounding boxes locating candidates in the search frame are additionally fed to the networks. The networks' outputs are normalised features lying on the same manifold. Again, the networks work as feature hierarchies aggregating fine-to-coarse spatial details, however in this work similarity is a priori defined by the training loss function. So SINT learns discriminating solely features of growing complexity with bounding boxes in query and search frame and an additional binary variable indicating correct and incorrect pairs measured by the Jaccard index. Training is done on images of objects from ALOV BIB002 . Training minimises a margin contrastive loss and uses pretraining on ImageNet. During tracking, the query is fed with the initial bounding box in the first frame resulting in a query feature vector. Inference samples candidates at radial positions and different scales and feeds the search at once resulting in feature vectors for each candidate. An offline learned ridge regressor refines finally position and scale of the winning candidate with maximal inner product to the query.