Visual Object Tracking with Autoencoder Representations

Date

2016-05-19

Author

Besbinar, Beril
Alatan, Abdullah Aydın

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

285
views

0
downloads

Deep learning is the discipline of training computational models that are composed of multiple layers and these methods have recently improved the state of the art in many areas as a virtue of large labeled datasets, increase in the computational power of current hardware and unsupervised training methods. Although such a dataset may not be available for lots of application areas, the representations obtained by the well-designed networks that have a large representation capacity and trained with enough data are claimed to have the ability to generalize for transfer learning. As an example application, in this work, we investigate the use of stacked autoencoders for visual object tracking, which is a challenging yet very important task in computer vision. Training of autoencoders is achieved via an auxiliary dataset and the resultant representations are utilized within the tracking-by-detection framework. Experiments, realized using a challenge toolkit, indicate that exploiting the intricate structure in auxiliary dataset via hierarchical representations contributes to the solution of visual object tracking problem.

Subject Keywords

Visual Object Tracking, Autoencoders, Deep Learning, Particle Filter

URI

https://hdl.handle.net/11511/55799

Conference Name

24th Signal Processing and Communication Application Conference (SIU)

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Multi-task Deep Neural Networks in Protein Function Prediction Rifaioğlu, Ahmet Süreyya; Doğan, Tunca; Martin, Maria Jesus; Atalay, Rengül; Atalay, Mehmet Volkan (2017-05-01) In recent years, deep learning algorithms have outperformed the state-of-the art methods in several areas thanks to the efficient methods for training and for preventing overfitting, advancement in computer hardware, the availability of vast amount data. The high performance of multi-task deep neural networks in drug discovery has attracted the attention to deep learning algorithms in bioinformatics area. Here, we proposed a hierarchical multi-task deep neural network architecture based on Gene Ontology (GO...
Hierarchical representations for visual object tracking by detection Beşbınar, Beril; Alatan, Abdullah Aydın; Department of Electrical and Electronics Engineering (2015) Deep learning is the discipline of training computational models that are composed of multiple layers and these methods have improved the state of the art in many areas such as visual object detection, scene understanding or speech recognition. Rebirth of these fairly old computational models is usually related to the availability of large datasets, increase in the computational power of current hardware and more recently proposed unsupervised training methods that exploit the internal structure of very lar...
Automated learning rate search using batch-level cross-validation Kabakcı, Duygu; Akbaş, Emre; Department of Computer Engineering (2019) Deep convolutional neural networks are being widely used in computer vision tasks, such as object recognition and detection, image segmentation and face recognition, with a variety of architectures. Deep learning researchers and practitioners have accumulated a significant amount of experience on training a wide variety of architectures on various datasets. However, given a specific network model and a dataset, obtaining the best model (i.e. the model giving the smallest test set error) while keeping the tr...
Automated learning rate search using batch-level cross-validation KABAKÇI, Duygu; Akbaş, Emre (2021-04-01) Deep learning researchers and practitioners have accumulated a significant amount of experience on training a wide variety of architectures on various datasets. However, given anetwork architecture and a dataset, obtaining the best model (i.e. the model giving the smallest test set error) while keeping the training time complexity low is still a challenging task. Hyper-parameters of deep neural networks, especially the learning rate and its (decay) schedule, highly affect the network's final performance. Th...
Deep Learning-Based Hybrid Approach for Phase Retrieval IŞIL, ÇAĞATAY; Öktem, Sevinç Figen; KOÇ, AYKUT (2019-06-24) We develop a phase retrieval algorithm that utilizes the hybrid-input-output (HIO) algorithm with a deep neural network (DNN). The DNN architecture, which is trained to remove the artifacts of HIO, is used iteratively with HIO to improve the reconstructions. The results demonstrate the effectiveness of the approach with little additional cost.

Citation Formats

B. Besbinar and A. A. Alatan, “Visual Object Tracking with Autoencoder Representations,” presented at the 24th Signal Processing and Communication Application Conference (SIU), Zonguldak, TURKEY, 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/55799.