26 research outputs found
Good Features to Correlate for Visual Tracking
During the recent years, correlation filters have shown dominant and
spectacular results for visual object tracking. The types of the features that
are employed in these family of trackers significantly affect the performance
of visual tracking. The ultimate goal is to utilize robust features invariant
to any kind of appearance change of the object, while predicting the object
location as properly as in the case of no appearance change. As the deep
learning based methods have emerged, the study of learning features for
specific tasks has accelerated. For instance, discriminative visual tracking
methods based on deep architectures have been studied with promising
performance. Nevertheless, correlation filter based (CFB) trackers confine
themselves to use the pre-trained networks which are trained for object
classification problem. To this end, in this manuscript the problem of learning
deep fully convolutional features for the CFB visual tracking is formulated. In
order to learn the proposed model, a novel and efficient backpropagation
algorithm is presented based on the loss function of the network. The proposed
learning framework enables the network model to be flexible for a custom
design. Moreover, it alleviates the dependency on the network trained for
classification. Extensive performance analysis shows the efficacy of the
proposed custom design in the CFB tracking framework. By fine-tuning the
convolutional parts of a state-of-the-art network and integrating this model to
a CFB tracker, which is the top performing one of VOT2016, 18% increase is
achieved in terms of expected average overlap, and tracking failures are
decreased by 25%, while maintaining the superiority over the state-of-the-art
methods in OTB-2013 and OTB-2015 tracking datasets.Comment: Accepted version of IEEE Transactions on Image Processin
Quadruplet Selection Methods for Deep Embedding Learning
Recognition of objects with subtle differences has been used in many
practical applications, such as car model recognition and maritime vessel
identification. For discrimination of the objects in fine-grained detail, we
focus on deep embedding learning by using a multi-task learning framework, in
which the hierarchical labels (coarse and fine labels) of the samples are
utilized both for classification and a quadruplet-based loss function. In order
to improve the recognition strength of the learned features, we present a novel
feature selection method specifically designed for four training samples of a
quadruplet. By experiments, it is observed that the selection of very hard
negative samples with relatively easy positive ones from the same coarse and
fine classes significantly increases some performance metrics in a fine-grained
dataset when compared to selecting the quadruplet samples randomly. The feature
embedding learned by the proposed method achieves favorable performance against
its state-of-the-art counterparts.Comment: 6 pages, 2 figures, accepted by IEEE ICIP 201
Comparison of Infrared and Visible Imagery for Object Tracking: Toward Trackers with Superior IR Performance
The subject of this paper is the visual object tracking in infrared (IR) videos. Our contribution is twofold. First, the performance behaviour of the state-of-the-art trackers is investigated via a comparative study using IR-visible band video conjugates, i.e., video pairs captured observing the same scene simultaneously, to identify the IR specific challenges. Second, we propose a novel ensemble based tracking method that is tuned to IR data. The proposed algorithm sequentially constructs and maintains a dynamical ensemble of simple correlators and produces tracking decisions by switching among the ensemble correlators depending on the target appearance in a computationally highly efficient manner We empirically show that our algorithm significantly outperforms the state-of-the-art trackers in our extensive set of experiments with IR imagery
LSOTB-TIR:A Large-Scale High-Diversity Thermal Infrared Object Tracking Benchmark
In this paper, we present a Large-Scale and high-diversity general Thermal
InfraRed (TIR) Object Tracking Benchmark, called LSOTBTIR, which consists of an
evaluation dataset and a training dataset with a total of 1,400 TIR sequences
and more than 600K frames. We annotate the bounding box of objects in every
frame of all sequences and generate over 730K bounding boxes in total. To the
best of our knowledge, LSOTB-TIR is the largest and most diverse TIR object
tracking benchmark to date. To evaluate a tracker on different attributes, we
define 4 scenario attributes and 12 challenge attributes in the evaluation
dataset. By releasing LSOTB-TIR, we encourage the community to develop deep
learning based TIR trackers and evaluate them fairly and comprehensively. We
evaluate and analyze more than 30 trackers on LSOTB-TIR to provide a series of
baselines, and the results show that deep trackers achieve promising
performance. Furthermore, we re-train several representative deep trackers on
LSOTB-TIR, and their results demonstrate that the proposed training dataset
significantly improves the performance of deep TIR trackers. Codes and dataset
are available at https://github.com/QiaoLiuHit/LSOTB-TIR.Comment: accepted by ACM Mutlimedia Conference, 202
Method For Learning Deep Features For Correlation Based Visual Tracking
In this paper, we address the problem of visual tracking by proposing a novel feature learning technique. Recently, correlation filter based methods have dominated the visual tracking community due to various reasons such as efficient dense matching in frequency domain and simple update strategy. Nevertheless, the studies of correlation filters utilize hand-crafted or pre-trained deep features of classification task without considering the correlation filter cost function. Thus, we attempt to learn deep convolutional features for correlation filter based object tracking. In our experiments on benchmark sequences, we observe a significant improvement over the hand-crafted features while decreasing the number of features utilized in the recent correlation filter based trackers
Extending Correlation Filter-Based Visual Tracking by Tree-Structured Ensemble and Spatial Windowing
Correlation filters have been successfully used in visual tracking due to their modeling power and computational efficiency. However, the state-of-the-art correlation filter-based (CFB) tracking algorithms tend to quickly discard the previous poses of the target, since they consider only a single filter in their models. On the contrary, our approach is to register multiple CFB trackers for previous poses and exploit the registered knowledge when an appearance change occurs. To this end, we propose a novel tracking algorithm [ of complexity O(D)] based on a large ensemble of CFB trackers. The ensemble [ of size O(2(D))] is organized over a binary tree (depth D), and learns the target appearance subspaces such that each constituent tracker becomes an expert of a certain appearance. During tracking, the proposed algorithm combines only the appearance-aware relevant experts to produce boosted tracking decisions. Additionally, we propose a versatile spatial windowing technique to enhance the individual expert trackers. For this purpose, spatial windows are learned for target objects as well as the correlation filters and then the windowed regions are processed for more robust correlations. In our extensive experiments on benchmark datasets, we achieve a substantial performance increase by using the proposed tracking algorithm together with the spatial windowing