Mosaics from arbitrary stereo video sequences

lthough mosaics are well established as a compact and non-redundant representation of image sequences, their application still suffers from restrictions of the camera motion or has to deal with parallax errors. We present an approach that allows construction of mosaics from arbitrary motion of a head-mounted camera pair. As there are no parallax errors when creating mosaics from planar objects, our approach first decomposes the scene into planar sub-scenes from stereo vision and creates a mosaic for each plane individually. The power of the presented mosaicing technique is evaluated in an office scenario, including the analysis of the parallax error

Cross-layer Optimized Wireless Video Surveillance

A wireless video surveillance system contains three major components, the video capture and preprocessing, the video compression and transmission over wireless sensor networks (WSNs), and the video analysis at the receiving end. The coordination of different components is important for improving the end-to-end video quality, especially under the communication resource constraint. Cross-layer control proves to be an efficient measure for optimal system configuration. In this dissertation, we address the problem of implementing cross-layer optimization in the wireless video surveillance system. The thesis work is based on three research projects. In the first project, a single PTU (pan-tilt-unit) camera is used for video object tracking. The problem studied is how to improve the quality of the received video by jointly considering the coding and transmission process. The cross-layer controller determines the optimal coding and transmission parameters, according to the dynamic channel condition and the transmission delay. Multiple error concealment strategies are developed utilizing the special property of the PTU camera motion. In the second project, the binocular PTU camera is adopted for video object tracking. The presented work studied the fast disparity estimation algorithm and the 3D video transcoding over the WSN for real-time applications. The disparity/depth information is estimated in a coarse-to-fine manner using both local and global methods. The transcoding is coordinated by the cross-layer controller based on the channel condition and the data rate constraint, in order to achieve the best view synthesis quality. The third project is applied for multi-camera motion capture in remote healthcare monitoring. The challenge is the resource allocation for multiple video sequences. The presented cross-layer design incorporates the delay sensitive, content-aware video coding and transmission, and the adaptive video coding and transmission to ensure the optimal and balanced quality for the multi-view videos. In these projects, interdisciplinary study is conducted to synergize the surveillance system under the cross-layer optimization framework. Experimental results demonstrate the efficiency of the proposed schemes. The challenges of cross-layer design in existing wireless video surveillance systems are also analyzed to enlighten the future work. Adviser: Song C

Cross-layer Optimized Wireless Video Surveillance

A wireless video surveillance system contains three major components, the video capture and preprocessing, the video compression and transmission over wireless sensor networks (WSNs), and the video analysis at the receiving end. The coordination of different components is important for improving the end-to-end video quality, especially under the communication resource constraint. Cross-layer control proves to be an efficient measure for optimal system configuration. In this dissertation, we address the problem of implementing cross-layer optimization in the wireless video surveillance system. The thesis work is based on three research projects. In the first project, a single PTU (pan-tilt-unit) camera is used for video object tracking. The problem studied is how to improve the quality of the received video by jointly considering the coding and transmission process. The cross-layer controller determines the optimal coding and transmission parameters, according to the dynamic channel condition and the transmission delay. Multiple error concealment strategies are developed utilizing the special property of the PTU camera motion. In the second project, the binocular PTU camera is adopted for video object tracking. The presented work studied the fast disparity estimation algorithm and the 3D video transcoding over the WSN for real-time applications. The disparity/depth information is estimated in a coarse-to-fine manner using both local and global methods. The transcoding is coordinated by the cross-layer controller based on the channel condition and the data rate constraint, in order to achieve the best view synthesis quality. The third project is applied for multi-camera motion capture in remote healthcare monitoring. The challenge is the resource allocation for multiple video sequences. The presented cross-layer design incorporates the delay sensitive, content-aware video coding and transmission, and the adaptive video coding and transmission to ensure the optimal and balanced quality for the multi-view videos. In these projects, interdisciplinary study is conducted to synergize the surveillance system under the cross-layer optimization framework. Experimental results demonstrate the efficiency of the proposed schemes. The challenges of cross-layer design in existing wireless video surveillance systems are also analyzed to enlighten the future work. Adviser: Song C

Algorithms and evaluation for object detection and tracking in computer vision

Vision-based object detection and tracking, especially for video surveillance applications, is studied from algorithms to performance evaluation. This dissertation is composed of four topics: (1) Background Modeling and Detection, (2) Performance Evaluation of Sensitive Target Detection, (3) Multi-view Multi-target Multi-Hypothesis Segmentation and Tracking of People, and (4) A Fine-Structure Image/Video Quality Measure. First, we present a real-time algorithm for foreground-background segmentation. It allows us to capture structural background variation due to periodic-like motion over a long period of time under limited memory. Our codebook-based representation is efficient in memory and speed compared with other background modeling techniques. Our method can handle scenes containing moving backgrounds or illumination variations, and it achieves robust detection for different types of videos. In addition to the basic algorithm, three features improving the algorithm are presented - Automatic Parameter Estimation, Layered Modeling/Detection and Adaptive Codebook Updating. Second, we introduce a performance evaluation methodology called Perturbation Detection Rate (PDR) analysis for measuring performance of foreground-background segmentation. It does not require foreground targets or knowledge of foreground distributions. It measures the sensitivity of a background subtraction algorithm in detecting possible low contrast targets against the background as a function of contrast. We compare four background subtraction algorithms using the methodology. Third, a multi-view multi-hypothesis approach to segmenting and tracking multiple persons on a ground plane is proposed. The tracking state space is the set of ground points of the people being tracked. During tracking, several iterations of segmentation are performed using information from human appearance models and ground plane homography. Two innovations are made in this chapter - (1) To more precisely locate the ground location of a person, all center vertical axes of the person across views are mapped to the top-view plane to find the intersection point. (2) To tackle the explosive state space due to multiple targets and views, iterative segmentation-searching is incorporated into a particle filtering framework. By searching for people's ground point locations from segmentations, a set of a few good particles can be identified, resulting in low computational cost. In addition, even if all the particles are away from the true ground point, some of them move towards the true one through the iterated process as long as they are located nearby. Finally, an objective no-reference measure is presented to assess fine-structure image/video quality. The proposed measure using local statistics reflects image degradation well in terms of noise and blur

Prioritizing Content of Interest in Multimedia Data Compression

Image and video compression techniques make data transmission and storage in digital multimedia systems more efficient and feasible for the system's limited storage and bandwidth. Many generic image and video compression techniques such as JPEG and H.264/AVC have been standardized and are now widely adopted. Despite their great success, we observe that these standard compression techniques are not the best solution for data compression in special types of multimedia systems such as microscopy videos and low-power wireless broadcast systems. In these application-specific systems where the content of interest in the multimedia data is known and well-defined, we should re-think the design of a data compression pipeline. We hypothesize that by identifying and prioritizing multimedia data's content of interest, new compression methods can be invented that are far more effective than standard techniques. In this dissertation, a set of new data compression methods based on the idea of prioritizing the content of interest has been proposed for three different kinds of multimedia systems. I will show that the key to designing efficient compression techniques in these three cases is to prioritize the content of interest in the data. The definition of the content of interest of multimedia data depends on the application. First, I show that for microscopy videos, the content of interest is defined as the spatial regions in the video frame with pixels that don't only contain noise. Keeping data in those regions with high quality and throwing out other information yields to a novel microscopy video compression technique. Second, I show that for a Bluetooth low energy beacon based system, practical multimedia data storage and transmission is possible by prioritizing content of interest. I designed custom image compression techniques that preserve edges in a binary image, or foreground regions of a color image of indoor or outdoor objects. Last, I present a new indoor Bluetooth low energy beacon based augmented reality system that integrates a 3D moving object compression method that prioritizes the content of interest.Doctor of Philosoph

A framework for evaluating stereo-based pedestrian detection techniques

Automated pedestrian detection, counting, and tracking have received significant attention in the computer vision community of late. As such, a variety of techniques have been investigated using both traditional 2-D computer vision techniques and, more recently, 3-D stereo information. However, to date, a quantitative assessment of the performance of stereo-based pedestrian detection has been problematic, mainly due to the lack of standard stereo-based test data and an agreed methodology for carrying out the evaluation. This has forced researchers into making subjective comparisons between competing approaches. In this paper, we propose a framework for the quantitative evaluation of a short-baseline stereo-based pedestrian detection system. We provide freely available synthetic and real-world test data and recommend a set of evaluation metrics. This allows researchers to benchmark systems, not only with respect to other stereo-based approaches, but also with more traditional 2-D approaches. In order to illustrate its usefulness, we demonstrate the application of this framework to evaluate our own recently proposed technique for pedestrian detection and tracking

Visual / acoustic detection and localisation in embedded systems

©Cranfield UniversityThe continuous miniaturisation of sensing and processing technologies is increasingly offering a variety of embedded platforms, enabling the accomplishment of a broad range of tasks using such systems. Motivated by these advances, this thesis investigates embedded detection and localisation solutions using vision and acoustic sensors. Focus is particularly placed on surveillance applications using sensor networks. Existing vision-based detection solutions for embedded systems suffer from the sensitivity to environmental conditions. In the literature, there seems to be no algorithm able to simultaneously tackle all the challenges inherent to real-world videos. Regarding the acoustic modality, many research works have investigated acoustic source localisation solutions in distributed sensor networks. Nevertheless, it is still a challenging task to develop an ecient algorithm that deals with the experimental issues, to approach the performance required by these systems and to perform the data processing in a distributed and robust manner. The movement of scene objects is generally accompanied with sound emissions with features that vary from an environment to another. Therefore, considering the combination of the visual and acoustic modalities would offer a significant opportunity for improving the detection and/or localisation using the described platforms. In the light of the described framework, we investigate in the first part of the thesis the use of a cost-effective visual based method that can deal robustly with the issue of motion detection in static, dynamic and moving background conditions. For motion detection in static and dynamic backgrounds, we present the development and the performance analysis of a spatio- temporal form of the Gaussian mixture model. On the other hand, the problem of motion detection in moving backgrounds is addressed by accounting for registration errors in the captured images. By adopting a robust optimisation technique that takes into account the uncertainty about the visual measurements, we show that high detection accuracy can be achieved. In the second part of this thesis, we investigate solutions to the problem of acoustic source localisation using a trust region based optimisation technique. The proposed method shows an overall higher accuracy and convergence improvement compared to a linear-search based method. More importantly, we show that through characterising the errors in measurements, which is a common problem for such platforms, higher accuracy in the localisation can be attained. The last part of this work studies the different possibilities of combining visual and acoustic information in a distributed sensors network. In this context, we first propose to include the acoustic information in the visual model. The obtained new augmented model provides promising improvements in the detection and localisation processes. The second investigated solution consists in the fusion of the measurements coming from the different sensors. An evaluation of the accuracy of localisation and tracking using a centralised/decentralised architecture is conducted in various scenarios and experimental conditions. Results have shown the capability of this fusion approach to yield higher accuracy in the localisation and tracking of an active acoustic source than by using a single type of data

Exploring Motion Signatures for Vision-Based Tracking, Recognition and Navigation

As cameras become more and more popular in intelligent systems, algorithms and systems for understanding video data become more and more important. There is a broad range of applications, including object detection, tracking, scene understanding, and robot navigation. Besides the stationary information, video data contains rich motion information of the environment. Biological visual systems, like human and animal eyes, are very sensitive to the motion information. This inspires active research on vision-based motion analysis in recent years. The main focus of motion analysis has been on low level motion representations of pixels and image regions. However, the motion signatures can benefit a broader range of applications if further in-depth analysis techniques are developed. In this dissertation, we mainly discuss how to exploit motion signatures to solve problems in two applications: object recognition and robot navigation. First, we use bird species recognition as the application to explore motion signatures for object recognition. We begin with study of the periodic wingbeat motion of flying birds. To analyze the wing motion of a flying bird, we establish kinematics models for bird wings, and obtain wingbeat periodicity in image frames after the perspective projection. Time series of salient extremities on bird images are extracted, and the wingbeat frequency is acquired for species classification. Physical experiments show that the frequency based recognition method is robust to segmentation errors and measurement lost up to 30%. In addition to the wing motion, the body motion of the bird is also analyzed to extract the flying velocity in 3D space. An interacting multi-model approach is then designed to capture the combined object motion patterns and different environment conditions. The proposed systems and algorithms are tested in physical experiments, and the results show a false positive rate of around 20% with a low false negative rate close to zero. Second, we explore motion signatures for vision-based vehicle navigation. We discover that motion vectors (MVs) encoded in Moving Picture Experts Group (MPEG) videos provide rich information of the motion in the environment, which can be used to reconstruct the vehicle ego-motion and the structure of the scene. However, MVs suffer from high noise level. To handle the challenge, an error propagation model for MVs is first proposed. Several steps, including MV merging, plane-at-infinity elimination, and planar region extraction, are designed to further reduce noises. The extracted planes are used as landmarks in an extended Kalman filter (EKF) for simultaneous localization and mapping. Results show that the algorithm performs localization and plane mapping with a relative trajectory error below 5:1%. Exploiting the fact that MVs encodes both environment information and moving obstacles, we further propose to track moving objects at the same time of localization and mapping. This enables the two critical navigation functionalities, localization and obstacle avoidance, to be performed in a single framework. MVs are labeled as stationary or moving according to their consistency to geometric constraints. Therefore, the extracted planes are separated into moving objects and the stationary scene. Multiple EKFs are used to track the static scene and the moving objects simultaneously. In physical experiments, we show a detection rate of moving objects at 96:6% and a mean absolute localization error below 3:5 meters