Single-target tracking of arbitrary objects using multi-layered features and contextual information

This thesis investigated single-target tracking of arbitrary objects. Tracking is a difficult problem due to a variety of challenges such as significant deformations of the target, occlusions, illumination variations, background clutter and camouflage. To achieve robust tracking performance under these severe conditions, this thesis proposed firstly a novel RGB single-target tracker which models the target with multi-layered features and contextual information. The proposed algorithm was tested on two different tracking benchmarks, i.e., VTB and VOT, where it demonstrated significantly more robust performance than other state-of-the-art RGB trackers. Proposed secondly was an extension of the designed RGB tracker to handle RGB-D images using both temporal and spatial constraints to exploit depth information more robustly. For evaluation, the thesis introduced a new RGB-D benchmark dataset with per-frame annotated attributes and extensive bias analysis, on which the proposed tracker achieved the best results. Proposed thirdly was a new tracking approach to handle camouflage problems in highly cluttered scenes exploiting global dynamic constraints from the context. To evaluate the tracker, a benchmark dataset was augmented with a new set of clutter sub-attributes. Using this dataset, it was demonstrated that the proposed method outperforms other state-of-the-art single target trackers on highly cluttered scenes

Staple: Complementary Learners for Real-Time Tracking

Correlation Filter-based trackers have recently achieved excellent performance, showing great robustness to challenging situations exhibiting motion blur and illumination changes. However, since the model that they learn depends strongly on the spatial layout of the tracked object, they are notoriously sensitive to deformation. Models based on colour statistics have complementary traits: they cope well with variation in shape, but suffer when illumination is not consistent throughout a sequence. Moreover, colour distributions alone can be insufficiently discriminative. In this paper, we show that a simple tracker combining complementary cues in a ridge regression framework can operate faster than 80 FPS and outperform not only all entries in the popular VOT14 competition, but also recent and far more sophisticated trackers according to multiple benchmarks.Comment: To appear in CVPR 201

Multi-target tracking using appearance models for identity maintenance

This thesis considers perception systems for urban environments. It focuses on the task of tracking dynamic objects and in particular on methods that can maintain the identities of targets through periods of ambiguity. Examples of such ambiguous situations occur when targets interact with each other, or when they are occluded by other objects or the environment. With the development of self driving cars, the push for autonomous delivery of packages, and an increasing use of technology for security, surveillance and public-safety applications, robust perception in crowded urban spaces is more important than ever before. A critical part of perception systems is the ability to understand the motion of objects in a scene. Tracking strategies that merge closely-spaced targets together into groups have been shown to offer improved robustness, but in doing so sacrifice the concept of target identity. Additionally, the primary sensor used for the tracking task may not provide the information required to reason about the identity of individual objects. There are three primary contributions in this work. The first is the development of 3D lidar tracking methods with improved ability to track closely-spaced targets and that can determine when target identities have become ambiguous. Secondly, this thesis defines appearance models suitable for the task of determining the identities of previously-observed targets, which may include the use of data from additional sensing modalities. The final contribution of this work is the combination of lidar tracking and appearance modelling, to enable the clarification of target identities in the presence of ambiguities caused by scene complexity. The algorithms presented in this work are validated on both carefully controlled and unconstrained datasets. The experiments show that in complex dynamic scenes with interacting targets, the proposed methods achieve significant improvements in tracking performance

Visual Object Tracking: The Initialisation Problem

Model initialisation is an important component of object tracking. Tracking algorithms are generally provided with the first frame of a sequence and a bounding box (BB) indicating the location of the object. This BB may contain a large number of background pixels in addition to the object and can lead to parts-based tracking algorithms initialising their object models in background regions of the BB. In this paper, we tackle this as a missing labels problem, marking pixels sufficiently away from the BB as belonging to the background and learning the labels of the unknown pixels. Three techniques, One-Class SVM (OC-SVM), Sampled-Based Background Model (SBBM) (a novel background model based on pixel samples), and Learning Based Digital Matting (LBDM), are adapted to the problem. These are evaluated with leave-one-video-out cross-validation on the VOT2016 tracking benchmark. Our evaluation shows both OC-SVMs and SBBM are capable of providing a good level of segmentation accuracy but are too parameter-dependent to be used in real-world scenarios. We show that LBDM achieves significantly increased performance with parameters selected by cross validation and we show that it is robust to parameter variation.Comment: 15th Conference on Computer and Robot Vision (CRV 2018). Source code available at https://github.com/georgedeath/initialisation-proble

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