Search Tracker: Human-derived object tracking in-the-wild through large-scale search and retrieval

Humans use context and scene knowledge to easily localize moving objects in conditions of complex illumination changes, scene clutter and occlusions. In this paper, we present a method to leverage human knowledge in the form of annotated video libraries in a novel search and retrieval based setting to track objects in unseen video sequences. For every video sequence, a document that represents motion information is generated. Documents of the unseen video are queried against the library at multiple scales to find videos with similar motion characteristics. This provides us with coarse localization of objects in the unseen video. We further adapt these retrieved object locations to the new video using an efficient warping scheme. The proposed method is validated on in-the-wild video surveillance datasets where we outperform state-of-the-art appearance-based trackers. We also introduce a new challenging dataset with complex object appearance changes.Comment: Under review with the IEEE Transactions on Circuits and Systems for Video Technolog

Gaussian mixture model classifiers for detection and tracking in UAV video streams.

Masters Degree. University of KwaZulu-Natal, Durban.Manual visual surveillance systems are subject to a high degree of human-error and operator fatigue. The automation of such systems often employs detectors, trackers and classifiers as fundamental building blocks. Detection, tracking and classification are especially useful and challenging in Unmanned Aerial Vehicle (UAV) based surveillance systems. Previous solutions have addressed challenges via complex classification methods. This dissertation proposes less complex Gaussian Mixture Model (GMM) based classifiers that can simplify the process; where data is represented as a reduced set of model parameters, and classification is performed in the low dimensionality parameter-space. The specification and adoption of GMM based classifiers on the UAV visual tracking feature space formed the principal contribution of the work. This methodology can be generalised to other feature spaces. This dissertation presents two main contributions in the form of submissions to ISI accredited journals. In the first paper, objectives are demonstrated with a vehicle detector incorporating a two stage GMM classifier, applied to a single feature space, namely Histogram of Oriented Gradients (HoG). While the second paper demonstrates objectives with a vehicle tracker using colour histograms (in RGB and HSV), with Gaussian Mixture Model (GMM) classifiers and a Kalman filter. The proposed works are comparable to related works with testing performed on benchmark datasets. In the tracking domain for such platforms, tracking alone is insufficient. Adaptive detection and classification can assist in search space reduction, building of knowledge priors and improved target representations. Results show that the proposed approach improves performance and robustness. Findings also indicate potential further enhancements such as a multi-mode tracker with global and local tracking based on a combination of both papers

Object Association Across Multiple Moving Cameras In Planar Scenes

In this dissertation, we address the problem of object detection and object association across multiple cameras over large areas that are well modeled by planes. We present a unifying probabilistic framework that captures the underlying geometry of planar scenes, and present algorithms to estimate geometric relationships between different cameras, which are subsequently used for co-operative association of objects. We first present a local1 object detection scheme that has three fundamental innovations over existing approaches. First, the model of the intensities of image pixels as independent random variables is challenged and it is asserted that useful correlation exists in intensities of spatially proximal pixels. This correlation is exploited to sustain high levels of detection accuracy in the presence of dynamic scene behavior, nominal misalignments and motion due to parallax. By using a non-parametric density estimation method over a joint domain-range representation of image pixels, complex dependencies between the domain (location) and range (color) are directly modeled. We present a model of the background as a single probability density. Second, temporal persistence is introduced as a detection criterion. Unlike previous approaches to object detection that detect objects by building adaptive models of the background, the foreground is modeled to augment the detection of objects (without explicit tracking), since objects detected in the preceding frame contain substantial evidence for detection in the current frame. Finally, the background and foreground models are used competitively in a MAP-MRF decision framework, stressing spatial context as a condition of detecting interesting objects and the posterior function is maximized efficiently by finding the minimum cut of a capacitated graph. Experimental validation of the method is performed and presented on a diverse set of data. We then address the problem of associating objects across multiple cameras in planar scenes. Since cameras may be moving, there is a possibility of both spatial and temporal non-overlap in the fields of view of the camera. We first address the case where spatial and temporal overlap can be assumed. Since the cameras are moving and often widely separated, direct appearance-based or proximity-based constraints cannot be used. Instead, we exploit geometric constraints on the relationship between the motion of each object across cameras, to test multiple correspondence hypotheses, without assuming any prior calibration information. Here, there are three contributions. First, we present a statistically and geometrically meaningful means of evaluating a hypothesized correspondence between multiple objects in multiple cameras. Second, since multiple cameras exist, ensuring coherency in association, i.e. transitive closure is maintained between more than two cameras, is an essential requirement. To ensure such coherency we pose the problem of object associating across cameras as a k-dimensional matching and use an approximation to find the association. We show that, under appropriate conditions, re-entering objects can also be re-associated to their original labels. Third, we show that as a result of associating objects across the cameras, a concurrent visualization of multiple aerial video streams is possible. Results are shown on a number of real and controlled scenarios with multiple objects observed by multiple cameras, validating our qualitative models. Finally, we present a unifying framework for object association across multiple cameras and for estimating inter-camera homographies between (spatially and temporally) overlapping and non-overlapping cameras, whether they are moving or non-moving. By making use of explicit polynomial models for the kinematics of objects, we present algorithms to estimate inter-frame homographies. Under an appropriate measurement noise model, an EM algorithm is applied for the maximum likelihood estimation of the inter-camera homographies and kinematic parameters. Rather than fit curves locally (in each camera) and match them across views, we present an approach that simultaneously refines the estimates of inter-camera homographies and curve coefficients globally. We demonstrate the efficacy of the approach on a number of real sequences taken from aerial cameras, and report quantitative performance during simulations

The application of classical conditioning to the machine learning of a commonsense knowledge of visual events

In the field of artificial intelligence, possession of commonsense knowledge has long been considered to be a requirementto construct a machine that possesses artificial general intelligence. The conventional approach to providing this commonsense knowledge is to manually encode the required knowledge, a process that is both tedious and costly. After an analysis of classical conditioning, it was deemed that constructing a system based upon the stimulusstimulus interpretation of classical conditioning could allow for commonsense knowledge to be learned through a machine directly and passively observing its environment. Based upon these principles, a system was constructed that uses a stream of events, that have been observed within the environment, to learn rules regarding what event is likely to follow after the observation of another event. The system makes use of a feedback loop between three sub-systems: one that associates events that occur together, a second that accumulates evidence that a given association is significant and a third that recognises the significant associations. The recognition of past associations allows for both the creation of evidence for and against the existence of a particular association, and also allows for more complex associations to be created by treating instances of strongly associated event pairs to be themselves events. Testing the abilities of the system involved simulating the three different learning environments. The results found that measures of significance based on classical conditioning generally outperformed a probability-based measure. This thesis contributes a theory of how a stimulus-stimulus interpretation classical conditioning can be used to create commonsense knowledge and an observation that a significant sub-set of classical conditioning phenomena likely exist to aid in the elimination of noise. This thesis also represents a significant departure from existing reinforcement learning systems as the system presented in this thesis does not perform any form of action selection

SeaVipers - Computer Vision and Inertial Position Reference Sensor System (CVIPRSS)

This work describes the design and development of an optical, Computer Vision (CV) based sensor for use as a Position Reference System (PRS) in Dynamic Positioning (DP). Using a combination of robotics and CV techniques, the sensor provides range and heading information to a selected reference object. The proposed optical system is superior to existing ones because it does not depend upon special reflectors nor does it require a lengthy set-up time. This system, the Computer Vision and Inertial Position Reference Sensor System (CVIPRSS, pronounced \nickname), combines a laser rangefinder, infrared camera, and a pan--tilt unit with the robust TLD (Tracking--Learning--Detection) object tracker. In this work, a \nickname ~prototype is evaluated, showing promising results as viable PRS with research, commercial, and industrial applications

Group Membership Prediction

The group membership prediction (GMP) problem involves predicting whether or not a collection of instances share a certain semantic property. For instance, in kinship verification given a collection of images, the goal is to predict whether or not they share a {\it familial} relationship. In this context we propose a novel probability model and introduce latent {\em view-specific} and {\em view-shared} random variables to jointly account for the view-specific appearance and cross-view similarities among data instances. Our model posits that data from each view is independent conditioned on the shared variables. This postulate leads to a parametric probability model that decomposes group membership likelihood into a tensor product of data-independent parameters and data-dependent factors. We propose learning the data-independent parameters in a discriminative way with bilinear classifiers, and test our prediction algorithm on challenging visual recognition tasks such as multi-camera person re-identification and kinship verification. On most benchmark datasets, our method can significantly outperform the current state-of-the-art.Comment: accepted for ICCV 201

Textile Fingerprinting for Dismount Analysis in the Visible, Near, and Shortwave Infrared Domain

The ability to accurately and quickly locate an individual, or a dismount, is useful in a variety of situations and environments. A dismount\u27s characteristics such as their gender, height, weight, build, and ethnicity could be used as discriminating factors. Hyperspectral imaging (HSI) is widely used in efforts to identify materials based on their spectral signatures. More specifically, HSI has been used for skin and clothing classification and detection. The ability to detect textiles (clothing) provides a discriminating factor that can aid in a more comprehensive detection of dismounts. This thesis demonstrates the application of several feature selection methods (i.e., support vector machines with recursive feature reduction, fast correlation based filter) in highly dimensional data collected from a spectroradiometer. The classification of the data is accomplished with the selected features and artificial neural networks. A model for uniquely identifying (fingerprinting) textiles are designed, where color and composition are determined in order to fingerprint a specific textile. An artificial neural network is created based on the knowledge of the textile\u27s color and composition, providing a uniquely identifying fingerprinting of a textile. Results show 100% accuracy for color and composition classification, and 98% accuracy for the overall textile fingerprinting process