DroTrack: High-speed Drone-based Object Tracking Under Uncertainty

We present DroTrack, a high-speed visual single-object tracking framework for drone-captured video sequences. Most of the existing object tracking methods are designed to tackle well-known challenges, such as occlusion and cluttered backgrounds. The complex motion of drones, i.e., multiple degrees of freedom in three-dimensional space, causes high uncertainty. The uncertainty problem leads to inaccurate location predictions and fuzziness in scale estimations. DroTrack solves such issues by discovering the dependency between object representation and motion geometry. We implement an effective object segmentation based on Fuzzy C Means (FCM). We incorporate the spatial information into the membership function to cluster the most discriminative segments. We then enhance the object segmentation by using a pre-trained Convolution Neural Network (CNN) model. DroTrack also leverages the geometrical angular motion to estimate a reliable object scale. We discuss the experimental results and performance evaluation using two datasets of 51,462 drone-captured frames. The combination of the FCM segmentation and the angular scaling increased DroTrack precision by up to $9\%$ and decreased the centre location error by $162$ pixels on average. DroTrack outperforms all the high-speed trackers and achieves comparable results in comparison to deep learning trackers. DroTrack offers high frame rates up to 1000 frame per second (fps) with the best location precision, more than a set of state-of-the-art real-time trackers.Comment: 10 pages, 12 figures, FUZZ-IEEE 202

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

Data-Driven Shape Analysis and Processing

Data-driven methods play an increasingly important role in discovering geometric, structural, and semantic relationships between 3D shapes in collections, and applying this analysis to support intelligent modeling, editing, and visualization of geometric data. In contrast to traditional approaches, a key feature of data-driven approaches is that they aggregate information from a collection of shapes to improve the analysis and processing of individual shapes. In addition, they are able to learn models that reason about properties and relationships of shapes without relying on hard-coded rules or explicitly programmed instructions. We provide an overview of the main concepts and components of these techniques, and discuss their application to shape classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis, through reviewing the literature and relating the existing works with both qualitative and numerical comparisons. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing.Comment: 10 pages, 19 figure

A brain-machine interface for assistive robotic control

Brain-machine interfaces (BMIs) are the only currently viable means of communication for many individuals suffering from locked-in syndrome (LIS) – profound paralysis that results in severely limited or total loss of voluntary motor control. By inferring user intent from task-modulated neurological signals and then translating those intentions into actions, BMIs can enable LIS patients increased autonomy. Significant effort has been devoted to developing BMIs over the last three decades, but only recently have the combined advances in hardware, software, and methodology provided a setting to realize the translation of this research from the lab into practical, real-world applications. Non-invasive methods, such as those based on the electroencephalogram (EEG), offer the only feasible solution for practical use at the moment, but suffer from limited communication rates and susceptibility to environmental noise. Maximization of the efficacy of each decoded intention, therefore, is critical. This thesis addresses the challenge of implementing a BMI intended for practical use with a focus on an autonomous assistive robot application. First an adaptive EEG- based BMI strategy is developed that relies upon code-modulated visual evoked potentials (c-VEPs) to infer user intent. As voluntary gaze control is typically not available to LIS patients, c-VEP decoding methods under both gaze-dependent and gaze- independent scenarios are explored. Adaptive decoding strategies in both offline and online task conditions are evaluated, and a novel approach to assess ongoing online BMI performance is introduced. Next, an adaptive neural network-based system for assistive robot control is presented that employs exploratory learning to achieve the coordinated motor planning needed to navigate toward, reach for, and grasp distant objects. Exploratory learning, or “learning by doing,” is an unsupervised method in which the robot is able to build an internal model for motor planning and coordination based on real-time sensory inputs received during exploration. Finally, a software platform intended for practical BMI application use is developed and evaluated. Using online c-VEP methods, users control a simple 2D cursor control game, a basic augmentative and alternative communication tool, and an assistive robot, both manually and via high-level goal-oriented commands

SeqTrack: Sequence to Sequence Learning for Visual Object Tracking

In this paper, we present a new sequence-to-sequence learning framework for visual tracking, dubbed SeqTrack. It casts visual tracking as a sequence generation problem, which predicts object bounding boxes in an autoregressive fashion. This is different from prior Siamese trackers and transformer trackers, which rely on designing complicated head networks, such as classification and regression heads. SeqTrack only adopts a simple encoder-decoder transformer architecture. The encoder extracts visual features with a bidirectional transformer, while the decoder generates a sequence of bounding box values autoregressively with a causal transformer. The loss function is a plain cross-entropy. Such a sequence learning paradigm not only simplifies tracking framework, but also achieves competitive performance on benchmarks. For instance, SeqTrack gets 72.5% AUC on LaSOT, establishing a new state-of-the-art performance. Code and models are available at here.Comment: CVPR2023 pape

Linear Regression and Unsupervised Learning For Tracking and Embodied Robot Control.

Computer vision problems, such as tracking and robot navigation, tend to be solved using models of the objects of interest to the problem. These models are often either hard-coded, or learned in a supervised manner. In either case, an engineer is required to identify the visual information that is important to the task, which is both time consuming and problematic. Issues with these engineered systems relate to the ungrounded nature of the knowledge imparted by the engineer, where the systems have no meaning attached to the representations. This leads to systems that are brittle and are prone to failure when expected to act in environments not envisaged by the engineer. The work presented in this thesis removes the need for hard-coded or engineered models of either visual information representations or behaviour. This is achieved by developing novel approaches for learning from example, in both input (percept) and output (action) spaces. This approach leads to the development of novel feature tracking algorithms, and methods for robot control. Applying this approach to feature tracking, unsupervised learning is employed, in real time, to build appearance models of the target that represent the input space structure, and this structure is exploited to partition banks of computationally efficient, linear regression based target displacement estimators. This thesis presents the first application of regression based methods to the problem of simultaneously modeling and tracking a target object. The computationally efficient Linear Predictor (LP) tracker is investigated, along with methods for combining and weighting flocks of LP’s. The tracking algorithms developed operate with accuracy comparable to other state of the art online approaches and with a significant gain in computational efficiency. This is achieved as a result of two specific contributions. First, novel online approaches for the unsupervised learning of modes of target appearance that identify aspects of the target are introduced. Second, a general tracking framework is developed within which the identified aspects of the target are adaptively associated to subsets of a bank of LP trackers. This results in the partitioning of LP’s and the online creation of aspect specific LP flocks that facilitate tracking through significant appearance changes. Applying the approach to the percept action domain, unsupervised learning is employed to discover the structure of the action space, and this structure is used in the formation of meaningful perceptual categories, and to facilitate the use of localised input-output (percept-action) mappings. This approach provides a realisation of an embodied and embedded agent that organises its perceptual space and hence its cognitive process based on interactions with its environment. Central to the proposed approach is the technique of clustering an input-output exemplar set, based on output similarity, and using the resultant input exemplar groupings to characterise a perceptual category. All input exemplars that are coupled to a certain class of outputs form a category - the category of a given affordance, action or function. In this sense the formed perceptual categories have meaning and are grounded in the embodiment of the agent. The approach is shown to identify the relative importance of perceptual features and is able to solve percept-action tasks, defined only by demonstration, in previously unseen situations. Within this percept-action learning framework, two alternative approaches are developed. The first approach employs hierarchical output space clustering of point-to-point mappings, to achieve search efficiency and input and output space generalisation as well as a mechanism for identifying the important variance and invariance in the input space. The exemplar hierarchy provides, in a single structure, a mechanism for classifying previously unseen inputs and generating appropriate outputs. The second approach to a percept-action learning framework integrates the regression mappings used in the feature tracking domain, with the action space clustering and imitation learning techniques developed in the percept-action domain. These components are utilised within a novel percept-action data mining methodology, that is able to discover the visual entities that are important to a specific problem, and to map from these entities onto the action space. Applied to the robot control task, this approach allows for real-time generation of continuous action signals, without the use of any supervision or definition of representations or rules of behaviour

VISUAL TRACKING AND ILLUMINATION RECOVERY VIA SPARSE REPRESENTATION

Compressive sensing, or sparse representation, has played a fundamental role in many fields of science. It shows that the signals and images can be reconstructed from far fewer measurements than what is usually considered to be necessary. Sparsity leads to efficient estimation, efficient compression, dimensionality reduction, and efficient modeling. Recently, there has been a growing interest in compressive sensing in computer vision and it has been successfully applied to face recognition, background subtraction, object tracking and other problems. Sparsity can be achieved by solving the compressive sensing problem using L1 minimization. In this dissertation, we present the results of a study of applying sparse representation to illumination recovery, object tracking, and simultaneous tracking and recognition. Illumination recovery, also known as inverse lighting, is the problem of recovering an illumination distribution in a scene from the appearance of objects located in the scene. It is used for Augmented Reality, where the virtual objects match the existing image and cast convincing shadows on the real scene rendered with the recovered illumination. Shadows in a scene are caused by the occlusion of incoming light, and thus contain information about the lighting of the scene. Although shadows have been used in determining the 3D shape of the object that casts shadows onto the scene, few studies have focused on the illumination information provided by the shadows. In this dissertation, we recover the illumination of a scene from a single image with cast shadows given the geometry of the scene. The images with cast shadows can be quite complex and therefore cannot be well approximated by low-dimensional linear subspaces. However, in this study we show that the set of images produced by a Lambertian scene with cast shadows can be efficiently represented by a sparse set of images generated by directional light sources. We first model an image with cast shadows as composed of a diffusive part (without cast shadows) and a residual part that captures cast shadows. Then, we express the problem in an L1-regularized least squares formulation, with nonnegativity constraints (as light has to be nonnegative at any point in space). This sparse representation enjoys an effective and fast solution, thanks to recent advances in compressive sensing. In experiments on both synthetic and real data, our approach performs favorably in comparison to several previously proposed methods. Visual tracking, which consistently infers the motion of a desired target in a video sequence, has been an active and fruitful research topic in computer vision for decades. It has many practical applications such as surveillance, human computer interaction, medical imaging and so on. Many challenges to design a robust tracking algorithm come from the enormous unpredictable variations in the target, such as deformations, fast motion, occlusions, background clutter, and lighting changes. To tackle the challenges posed by tracking, we propose a robust visual tracking method by casting tracking as a sparse approximation problem in a particle filter framework. In this framework, occlusion, noise and other challenging issues are addressed seamlessly through a set of trivial templates. Specifically, to find the tracking target at a new frame, each target candidate is sparsely represented in the space spanned by target templates and trivial templates. The sparsity is achieved by solving an L1-regularized least squares problem. Then the candidate with the smallest projection error is taken as the tracking target. After that, tracking is continued using a Bayesian state inference framework in which a particle filter is used for propagating sample distributions over time. Three additional components further improve the robustness of our approach: 1) a velocity incorporated motion model that helps concentrate the samples on the true target location in the next frame, 2) the nonnegativity constraints that help filter out clutter that is similar to tracked targets in reversed intensity patterns, and 3) a dynamic template update scheme that keeps track of the most representative templates throughout the tracking procedure. We test the proposed approach on many challenging sequences involving heavy occlusions, drastic illumination changes, large scale changes, non-rigid object movement, out-of-plane rotation, and large pose variations. The proposed approach shows excellent performance in comparison with four previously proposed trackers. We also extend the work to simultaneous tracking and recognition in vehicle classification in IR video sequences. We attempt to resolve the uncertainties in tracking and recognition at the same time by introducing a static template set that stores target images in various conditions such as different poses, lighting, and so on. The recognition results at each frame are propagated to produce the final result for the whole video. The tracking result is evaluated at each frame and low confidence in tracking performance initiates a new cycle of tracking and classification. We demonstrate the robustness of the proposed method on vehicle tracking and classification using outdoor IR video sequences