Person re-Identification over distributed spaces and time

PhDReplicating the human visual system and cognitive abilities that the brain uses to process the information it receives is an area of substantial scientific interest. With the prevalence of video surveillance cameras a portion of this scientific drive has been into providing useful automated counterparts to human operators. A prominent task in visual surveillance is that of matching people between disjoint camera views, or re-identification. This allows operators to locate people of interest, to track people across cameras and can be used as a precursory step to multi-camera activity analysis. However, due to the contrasting conditions between camera views and their effects on the appearance of people re-identification is a non-trivial task. This thesis proposes solutions for reducing the visual ambiguity in observations of people between camera views This thesis first looks at a method for mitigating the effects on the appearance of people under differing lighting conditions between camera views. This thesis builds on work modelling inter-camera illumination based on known pairs of images. A Cumulative Brightness Transfer Function (CBTF) is proposed to estimate the mapping of colour brightness values based on limited training samples. Unlike previous methods that use a mean-based representation for a set of training samples, the cumulative nature of the CBTF retains colour information from underrepresented samples in the training set. Additionally, the bi-directionality of the mapping function is explored to try and maximise re-identification accuracy by ensuring samples are accurately mapped between cameras. Secondly, an extension is proposed to the CBTF framework that addresses the issue of changing lighting conditions within a single camera. As the CBTF requires manually labelled training samples it is limited to static lighting conditions and is less effective if the lighting changes. This Adaptive CBTF (A-CBTF) differs from previous approaches that either do not consider lighting change over time, or rely on camera transition time information to update. By utilising contextual information drawn from the background in each camera view, an estimation of the lighting change within a single camera can be made. This background lighting model allows the mapping of colour information back to the original training conditions and thus remove the need for 3 retraining. Thirdly, a novel reformulation of re-identification as a ranking problem is proposed. Previous methods use a score based on a direct distance measure of set features to form a correct/incorrect match result. Rather than offering an operator a single outcome, the ranking paradigm is to give the operator a ranked list of possible matches and allow them to make the final decision. By utilising a Support Vector Machine (SVM) ranking method, a weighting on the appearance features can be learned that capitalises on the fact that not all image features are equally important to re-identification. Additionally, an Ensemble-RankSVM is proposed to address scalability issues by separating the training samples into smaller subsets and boosting the trained models. Finally, the thesis looks at a practical application of the ranking paradigm in a real world application. The system encompasses both the re-identification stage and the precursory extraction and tracking stages to form an aid for CCTV operators. Segmentation and detection are combined to extract relevant information from the video, while several combinations of matching techniques are combined with temporal priors to form a more comprehensive overall matching criteria. The effectiveness of the proposed approaches is tested on datasets obtained from a variety of challenging environments including offices, apartment buildings, airports and outdoor public spaces

Enhanced target detection in CCTV network system using colour constancy

The focus of this research is to study how targets can be more faithfully detected in a multi-camera CCTV network system using spectral feature for the detection. The objective of the work is to develop colour constancy (CC) methodology to help maintain the spectral feature of the scene into a constant stable state irrespective of variable illuminations and camera calibration issues. Unlike previous work in the field of target detection, two versions of CC algorithms have been developed during the course of this work which are capable to maintain colour constancy for every image pixel in the scene: 1) a method termed as Enhanced Luminance Reflectance CC (ELRCC) which consists of a pixel-wise sigmoid function for an adaptive dynamic range compression, 2) Enhanced Target Detection and Recognition Colour Constancy (ETDCC) algorithm which employs a bidirectional pixel-wise non-linear transfer PWNLTF function, a centre-surround luminance enhancement and a Grey Edge white balancing routine. The effectiveness of target detections for all developed CC algorithms have been validated using multi-camera ‘Imagery Library for Intelligent Detection Systems’ (iLIDS), ‘Performance Evaluation of Tracking and Surveillance’ (PETS) and ‘Ground Truth Colour Chart’ (GTCC) datasets. It is shown that the developed CC algorithms have enhanced target detection efficiency by over 175% compared with that without CC enhancement. The contribution of this research has been one journal paper published in the Optical Engineering together with 3 conference papers in the subject of research

Scene Monitoring With A Forest Of Cooperative Sensors

In this dissertation, we present vision based scene interpretation methods for monitoring of people and vehicles, in real-time, within a busy environment using a forest of co-operative electro-optical (EO) sensors. We have developed novel video understanding algorithms with learning capability, to detect and categorize people and vehicles, track them with in a camera and hand-off this information across multiple networked cameras for multi-camera tracking. The ability to learn prevents the need for extensive manual intervention, site models and camera calibration, and provides adaptability to changing environmental conditions. For object detection and categorization in the video stream, a two step detection procedure is used. First, regions of interest are determined using a novel hierarchical background subtraction algorithm that uses color and gradient information for interest region detection. Second, objects are located and classified from within these regions using a weakly supervised learning mechanism based on co-training that employs motion and appearance features. The main contribution of this approach is that it is an online procedure in which separate views (features) of the data are used for co-training, while the combined view (all features) is used to make classification decisions in a single boosted framework. The advantage of this approach is that it requires only a few initial training samples and can automatically adjust its parameters online to improve the detection and classification performance. Once objects are detected and classified they are tracked in individual cameras. Single camera tracking is performed using a voting based approach that utilizes color and shape cues to establish correspondence in individual cameras. The tracker has the capability to handle multiple occluded objects. Next, the objects are tracked across a forest of cameras with non-overlapping views. This is a hard problem because of two reasons. First, the observations of an object are often widely separated in time and space when viewed from non-overlapping cameras. Secondly, the appearance of an object in one camera view might be very different from its appearance in another camera view due to the differences in illumination, pose and camera properties. To deal with the first problem, the system learns the inter-camera relationships to constrain track correspondences. These relationships are learned in the form of multivariate probability density of space-time variables (object entry and exit locations, velocities, and inter-camera transition times) using Parzen windows. To handle the appearance change of an object as it moves from one camera to another, we show that all color transfer functions from a given camera to another camera lie in a low dimensional subspace. The tracking algorithm learns this subspace by using probabilistic principal component analysis and uses it for appearance matching. The proposed system learns the camera topology and subspace of inter-camera color transfer functions during a training phase. Once the training is complete, correspondences are assigned using the maximum a posteriori (MAP) estimation framework using both the location and appearance cues. Extensive experiments and deployment of this system in realistic scenarios has demonstrated the robustness of the proposed methods. The proposed system was able to detect and classify targets, and seamlessly tracked them across multiple cameras. It also generated a summary in terms of key frames and textual description of trajectories to a monitoring officer for final analysis and response decision. This level of interpretation was the goal of our research effort, and we believe that it is a significant step forward in the development of intelligent systems that can deal with the complexities of real world scenarios

A Watch-List Based Classification System

Watch-list-based classification and verification is advantageous in a variety of surveillance applications. In this thesis, we present an approach for verifying if a query image lies in a predefined set of target samples (the watch-list) or not. This approach is particularly useful at identifying a small set of target subjects and therefore can render high levels of accuracy. Further, this approach can also be extended to identify the query image exactly out of the target samples. The three- stages approach proposed here consists of using a combination of color and texture features to represent the image and further using, Kernel Partial Least Squares for dimensionality reduction followed by a classifier. This approach provides improved accuracy as shown by experiments on two datasets

Object Tracking

Object tracking consists in estimation of trajectory of moving objects in the sequence of images. Automation of the computer object tracking is a difficult task. Dynamics of multiple parameters changes representing features and motion of the objects, and temporary partial or full occlusion of the tracked objects have to be considered. This monograph presents the development of object tracking algorithms, methods and systems. Both, state of the art of object tracking methods and also the new trends in research are described in this book. Fourteen chapters are split into two sections. Section 1 presents new theoretical ideas whereas Section 2 presents real-life applications. Despite the variety of topics contained in this monograph it constitutes a consisted knowledge in the field of computer object tracking. The intention of editor was to follow up the very quick progress in the developing of methods as well as extension of the application

Realtime Color Stereovision Processing

Recent developments in aviation have made micro air vehicles (MAVs) a reality. These featherweight palm-sized radio-controlled flying saucers embody the future of air-to-ground combat. No one has ever successfully implemented an autonomous control system for MAVs. Because MAVs are physically small with limited energy supplies, video signals offer superiority over radar for navigational applications. This research takes a step forward in real time machine vision processing. It investigates techniques for implementing a real time stereovision processing system using two miniature color cameras. The effects of poor-quality optics are overcome by a robust algorithm, which operates in real time and achieves frame rates up to 10 fps in ideal conditions. The vision system implements innovative work in the following five areas of vision processing: fast image registration preprocessing, object detection, feature correspondence, distortion-compensated ranging, and multi scale nominal frequency-based object recognition. Results indicate that the system can provide adequate obstacle avoidance feedback for autonomous vehicle control. However, typical relative position errors are about 10%-to high for surveillance applications. The range of operation is also limited to between 6 - 30 m. The root of this limitation is imprecise feature correspondence: with perfect feature correspondence the range would extend to between 0.5 - 30 m. Stereo camera separation limits the near range, while optical resolution limits the far range. Image frame sizes are 160x120 pixels. Increasing this size will improve far range characteristics but will also decrease frame rate. Image preprocessing proved to be less appropriate than precision camera alignment in this application. A proof of concept for object recognition shows promise for applications with more precise object detection. Future recommendations are offered in all five areas of vision processing

Data fusion in ubiquitous networked robot systems for urban services

There is a clear trend in the use of robots to accomplish services that can help humans. In this paper, robots acting in urban environments are considered for the task of person guiding. Nowadays, it is common to have ubiquitous sensors integrated within the buildings, such as camera networks, and wireless communications like 3G or WiFi. Such infrastructure can be directly used by robotic platforms. The paper shows how combining the information from the robots and the sensors allows tracking failures to be overcome, by being more robust under occlusion, clutter, and lighting changes. The paper describes the algorithms for tracking with a set of fixed surveillance cameras and the algorithms for position tracking using the signal strength received by a wireless sensor network (WSN). Moreover, an algorithm to obtain estimations on the positions of people from cameras on board robots is described. The estimate from all these sources are then combined using a decentralized data fusion algorithm to provide an increase in performance. This scheme is scalable and can handle communication latencies and failures. We present results of the system operating in real time on a large outdoor environment, including 22 nonoverlapping cameras, WSN, and several robots.Universidad Pablo de Olavide. Departamento de Deporte e InformáticaPostprin

HATSouth: a global network of fully automated identical wide-field telescopes

HATSouth is the world's first network of automated and homogeneous telescopes that is capable of year-round 24-hour monitoring of positions over an entire hemisphere of the sky. The primary scientific goal of the network is to discover and characterize a large number of transiting extrasolar planets, reaching out to long periods and down to small planetary radii. HATSouth achieves this by monitoring extended areas on the sky, deriving high precision light curves for a large number of stars, searching for the signature of planetary transits, and confirming planetary candidates with larger telescopes. HATSouth employs 6 telescope units spread over 3 locations with large longitude separation in the southern hemisphere (Las Campanas Observatory, Chile; HESS site, Namibia; Siding Spring Observatory, Australia). Each of the HATSouth units holds four 0.18m diameter f/2.8 focal ratio telescope tubes on a common mount producing an 8.2x8.2 arcdeg field, imaged using four 4Kx4K CCD cameras and Sloan r filters, to give a pixel scale of 3.7 arcsec/pixel. The HATSouth network is capable of continuously monitoring 128 square arc-degrees. We present the technical details of the network, summarize operations, and present weather statistics for the 3 sites. On average each of the 6 HATSouth units has conducted observations on ~500 nights over a 2-year time period, yielding a total of more than 1million science frames at 4 minute integration time, and observing ~10.65 hours per day on average. We describe the scheme of our data transfer and reduction from raw pixel images to trend-filtered light curves and transiting planet candidates. Photometric precision reaches ~6 mmag at 4-minute cadence for the brightest non-saturated stars at r~10.5. We present detailed transit recovery simulations to determine the expected yield of transiting planets from HATSouth. (abridged)Comment: 25 pages, 11 figures, 1 table, submitted to PAS

Lightfield Analysis and Its Applications in Adaptive Optics and Surveillance Systems

An image can only be as good as the optics of a camera or any other imaging system allows it to be. An imaging system is merely a transformation that takes a 3D world coordinate to a 2D image plane. This can be done through both linear/non-linear transfer functions. Depending on the application at hand it is easier to use some models of imaging systems over the others in certain situations. The most well-known models are the 1) Pinhole model, 2) Thin Lens Model and 3) Thick lens model for optical systems. Using light-field analysis the connection through these different models is described. A novel figure of merit is presented on using one optical model over the other for certain applications. After analyzing these optical systems, their applications in plenoptic cameras for adaptive optics applications are introduced. A new technique to use a plenoptic camera to extract information about a localized distorted planar wave front is described. CODEV simulations conducted in this thesis show that its performance is comparable to those of a Shack-Hartmann sensor and that they can potentially increase the dynamic range of angles that can be extracted assuming a paraxial imaging system. As a final application, a novel dual PTZ-surveillance system to track a target through space is presented. 22X optic zoom lenses on high resolution pan/tilt platforms recalibrate a master-slave relationship based on encoder readouts rather than complicated image processing algorithms for real-time target tracking. As the target moves out of a region of interest in the master camera, it is moved to force the target back into the region of interest. Once the master camera is moved, a precalibrated lookup table is interpolated to compute the relationship between the master/slave cameras. The homography that relates the pixels of the master camera to the pan/tilt settings of the slave camera then continue to follow the planar trajectories of targets as they move through space at high accuracies