[PDF] Top 20 Deep Learning Based Visual Tracking: A Review

... best tracking features in each video frame, an RNN constructing video memory state, and a reinforcement learning (RL) agent making target location ...The tracking problem is formulated as a ... See full document

... State Tracking: The process of constantly representing the state of the dialog is called di- alog state tracking ...State Tracking Chal- lenge (DSTC) (Williams et ...values based on a sequence ... See full document

... Socket programming [34] is helpful for communication between a server and a client that are on two different systems. It is a way of connecting two nodes where one node uses a particular IP address to reach to another ... See full document

... employing deep learning techniques have been ...of deep neural networks in smart applications was hindered by several shortcomings, such as poor recognition rates, slow processing of large datasets, ... See full document

... popular deep learning architecture for visual behavior detection to understand the visual context and interpret several objects and people within an ...for visual intelligence cannot be ... See full document

... part-based tracking algorithm based on spatio-temporal context learning, which includes a structural layout constraint and a model updating ...end-to-end deep regression model utilizing ... See full document

... Traditional object detection approaches used handcrafted features to great effect. Early proposals in the field of object detection mainly included the Viola Jones detector [7], HOG detector [8] and Deformable ... See full document

... (i.e., learning to hash method). The goal of learning to hash is to learn data-dependent and task-specific hash functions that yield compact binary codes to achieve good search accuracy, where sophisti- ... See full document

... computer based analysis and classification of physiological signals for healthcare applications [17, 18, ...is based on the idea of having an offline and online system, as shown in Figure 1 [21, ... See full document

... path tracking problem were accomplished in a case study of a cylindrical helix ...path tracking was designed by applying deep reinforcement learning DDPG ...actions based on positive ... See full document

... subspace learning [7, 27, 29] has been applied to visual tracking [11, 13, 24, 25, ...tensor-based tracking algorithms [13, 24, 25] conducted PCA on the mode-k unfolding ...out ... See full document

... Convolutional neural network (CNN) is a multi-layer sensor, and it is inspired by the biological visual neural mechanism. It consists of multiple convolution and subsampling layers, with the ability to ... See full document

... Deep learning, a family of machine learning algorithms, is inspired by the biological process of neural networks is dominating in many applications and proving its advantage over conventional machine ... See full document

... Video surveillance implies the likelihood of visual observation without being straightforwardly on location. Observation can be performed consistently as well as recorded for later survey through information ... See full document

... new deep neural architectures to train banks of texture ...of deep convolutional neural networks for automatic feature learning, led to the introduction of Fisher convolutional neural network and ... See full document

... contrary, deep regression networks that reformulate the correlation operation as a convolutional layer are fully differentiable and can be trained end to ...a deep regression tracker, when properly ... See full document

... The deep learning technology shows great promise in almost every field artificial intelligence, especially computer vision, such as image classification, detection and ...proposed deep ... See full document

... Initially, system tries to detect small UAVs which intrude and observe the horizon with the wide-angle static cam- era. In order to be coherent with the square input shape of the YOLO architecture, firstly we reshape the ... See full document

... of deep learning is that these layers of features are not designed by human engineers, they are learned from data using a general purpose learning pro- cedure ...[1]. Deep learning ... See full document

... For this system, ego-motion is estimated from two consecutive video frames. Instead of inputting the two images directly into the network, a flow image is computed using the FlowNetS architecture, and then that result is ... See full document