Deep similarity trackers are able to track above real-time speed. However, their accuracy is considerably lower than deep classification based trackers since they avoid valuable online cues. To feed the target-specific information for real-time object tracking, we propose a novel Siamese attention network. Different types of attention mechanisms are used to capture different contexts of target information and then learned knowledge is used to feed target cues at different representation levels of similarity tracking. In addition, an online learning mechanism is employed to utilise the available target-specific data. The proposed tracker reduces the impact of noise in the target template and improves the accuracy of similarity tracking by feeding target cues into the similarity search. Extensive evaluation performed on OTB-2013/50/100 and VOT2018 benchmark datasets demonstrate the proposed tracker outperforms state-of-the-art approaches while maintaining real-time tracking speed.

Visual tracking frameworks employing Convolutional Neural Networks (CNNs) have shown state-of-the-art performance due to their hierarchical feature representation. While classification and update based deep neural net tracking have shown good performance in terms of accuracy, they have poor tracking speed. On the other hand, recent matching based techniques using CNNs show higher than real-time speed in tracking but this speed is achieved at a considerably lower accuracy. To successfully manage the trade-off between accuracy and speed, we propose a novel CNN architecture for visual tracking. We achieve this trade-off balance by using an approach in which consecutive similar frames are processed with a similarity matching technique, and dissimilar frames are processed with a classification approach within the CNN architecture. The tracking speed is improved by avoiding unnecessary model updates through the measurement of similarity between adjacent frames, while the accuracy is maintained by adopting a classification approach when needed, with deeper level features. Extensive evaluation performed on a publicly available benchmark dataset demonstrates our proposed tracker shows competitive performance while maintaining near real-time speed.

Convolutional neural networks (CNNs) have been employed in visual tracking due to their rich levels of feature representation. While the learning capability of a CNN increases with its depth, unfortunately spatial information is diluted in deeper layers which hinders its important ability to localise targets. To successfully manage this trade-off, we propose a novel residual network based gating CNN architecture for object tracking. Our deep model connects the front and bottom convolutional features with a gate layer. This new network learns discriminative features while reducing the spatial information lost. This architecture is pre-trained to learn generic tracking characteristics. In online tracking, an efficient domain adaptation mechanism is used to accurately learn the target appearance with limited samples. Extensive evaluation performed on a publicly available benchmark dataset demonstrates our proposed tracker outperforms state-of-the-art approaches.

Advances in machine learning and image feature representations have led to great progress in pattern recognition approaches in recognising up to 1000 visual object categories. However, the human brain solves this problem effortlessly as it can recognise about 10000 to 100000 objects with a small number of examples. In recent years bag-of-features approach has proved to yield state-of-the-art performance in large scale evaluations. In such systems a visual codebook plays a crucial role. For constructing a codebook researchers cover a large-scale of training image set. But this brings up the issue of scalability. A large volume of training data becomes difficult to process whereas the high dimensional image representation could make many machine learning algorithms become inefficient or even a breakdown. In this work we investigate whether the dominant bag-of-features approach used in object recognition will continue significantly to improve with large training image set or not. We have validated a one-pass clustering algorithm to construct visual codebooks for object classification tasks on the PASCAL VOC Challenge image set. Our testing results show that adding more training images do not contribute significantly to increase the performance of classification but it increases the overall model complexity in terms of increased storage requirement and greater computational time. This study further suggests an alternative view to the community working with the patch-based object recognition to enforce retaining more discriminative descriptors rather than the reminiscent of the BIG data hypothesis.