Visual Object Tracking Using Deep Similarity Networks

Le suivi d’objets est une des tâches fondamentales de la vision par ordinateur. Étant donné une cible initiale dans la première trame, l’objectif du suivi est de trouver la nouvelle position de la cible dans les trames subséquentes. Les filtres de corrélation est une technique largement utilisée dans les applications de reconnaissance de formes pour mettre en correspondance des objets. Ainsi, de nombreuses méthodes de suivi sont basées sur les filtres de corrélation. Bien que ces méthodes de suivi atteignent un bon compromis entre la précision et la vitesse de suivi, leurs performances sont limitées par l’utilisation de caractéristiques d’apparence définies manuellement. Récemment, les méthodes d’apprentissage profond ont démontré des résultats impressionnants dans diverses tâches de vision par ordinateur. De ce fait, un grand nombre de méthodes de suivi basées sur l’apprentissage profond ont été proposées pour améliorer la robustesse et la capacité à discriminer les objets durant le suivi. Cependant, la représentation des caractéristiques de la cible par un seul réseau neuronal convolutif (RNC) n’est souvent pas assez discriminante. De plus, l’architecture du RNC doit être choisie avec soins pour réduire le temps de calcul lors du suivi. Dans notre travail, nous avons exploré différentes méthodes pour améliorer le pouvoir discriminant d’un RNC pour le suivi d’un objet. Nous avons proposé deux méthodes de suivi, MBST (Multi-Branch Siamese Tracker) et MFST (Multiple Features-Siamese Tracker). Les deux méthodes sont basées sur des RNC avec une architecture siamoise. Cette architecture définit le suivi comme un problème d’apprentissage par similarité. ----------ABSTRACT : In this thesis, we study visual object tracking using deep similarity networks. Visual object tracking is a fundamental task in computer vision. Many approaches based on correlation filters and deep learning have been proposed to solve this problem. Inspired by deep learning-based methods, we exploit the siamese network model to address object tracking, by formulating tracking as a similarity learning problem. Most deep learning methods only use features extracted from the last convolutional layer of a single model which lead their tracker to be prone to fail when target appearance changes significantly. Different features extracted from different convolutional models encode the target object in different ways. Trackers with diverse feature representations can be adapted more easily to challenging scenarios. Besides, the discriminative power of features from the last convolutional layer is very limited. Since convolutional features from different layers contain different levels of abstraction of the target object, discovering an appropriate scheme to fuse hierarchical features is also beneficial for tracking

Multi-Branch Siamese Networks with Online Selection for Object Tracking

In this paper, we propose a robust object tracking algorithm based on a branch selection mechanism to choose the most efficient object representations from multi-branch siamese networks. While most deep learning trackers use a single CNN for target representation, the proposed Multi-Branch Siamese Tracker (MBST) employs multiple branches of CNNs pre-trained for different tasks, and used for various target representations in our tracking method. With our branch selection mechanism, the appropriate CNN branch is selected depending on the target characteristics in an online manner. By using the most adequate target representation with respect to the tracked object, our method achieves real-time tracking, while obtaining improved performance compared to standard Siamese network trackers on object tracking benchmarks.Comment: ISVC2018, oral presentatio

Relation-aware Ensemble Learning for Knowledge Graph Embedding

Knowledge graph (KG) embedding is a fundamental task in natural language processing, and various methods have been proposed to explore semantic patterns in distinctive ways. In this paper, we propose to learn an ensemble by leveraging existing methods in a relation-aware manner. However, exploring these semantics using relation-aware ensemble leads to a much larger search space than general ensemble methods. To address this issue, we propose a divide-search-combine algorithm RelEns-DSC that searches the relation-wise ensemble weights independently. This algorithm has the same computation cost as general ensemble methods but with much better performance. Experimental results on benchmark datasets demonstrate the effectiveness of the proposed method in efficiently searching relation-aware ensemble weights and achieving state-of-the-art embedding performance. The code is public at https://github.com/LARS-research/RelEns.Comment: This short paper has been accepted by EMNLP 202

Data Release of the AST3-2 Automatic Survey from Dome A, Antarctica

AST3-2 is the second of the three Antarctic Survey Telescopes, aimed at wide-field time-domain optical astronomy. It is located at Dome A, Antarctica, which is by many measures the best optical astronomy site on the Earth's surface. Here we present the data from the AST3-2 automatic survey in 2016 and the photometry results. The median 5$\sigma$ limiting magnitude in $i$-band is 17.8 mag and the light curve precision is 4 mmag for bright stars. The data release includes photometry for over 7~million stars, from which over 3,500 variable stars were detected, with 70 of them newly discovered. We classify these new variables into different types by combining their light curve features with stellar properties from surveys such as StarHorse.Comment: 16 pages, 20 figures, accepted for publication in MNRA