Robust density modelling using the student's t-distribution for human action recognition

The extraction of human features from videos is often inaccurate and prone to outliers. Such outliers can severely affect density modelling when the Gaussian distribution is used as the model since it is highly sensitive to outliers. The Gaussian distribution is also often used as base component of graphical models for recognising human actions in the videos (hidden Markov model and others) and the presence of outliers can significantly affect the recognition accuracy. In contrast, the Student's t-distribution is more robust to outliers and can be exploited to improve the recognition rate in the presence of abnormal data. In this paper, we present an HMM which uses mixtures of t-distributions as observation probabilities and show how experiments over two well-known datasets (Weizmann, MuHAVi) reported a remarkable improvement in classification accuracy. © 2011 IEEE

Compressive Sensing for Target Detection and Tracking within Wireless Visual Sensor Networks-based Surveillance applications

Wireless Visual Sensor Networks (WVSNs) have gained significant importance in the last few years and have emerged in several distinctive applications. The main aim of this research is to investigate the use of adaptive Compressive Sensing (CS) for efficient target detection and tracking in WVSN-based surveillance applications. CS is expected to overcome the WVSN resource constraints such as memory limitation, communication bandwidth and battery constraints. In addition, adaptive CS dynamically chooses variable compression rates according to different data sets to represent captured images in an efficient way hence saving energy and memory space. In this work, a literature review on compressive sensing, target detection and tracking for WVSN is carried out to investigate existing techniques. Only single view target tracking is considered to keep minimum number of visual sensor nodes in a wake-up state to optimize the use of nodes and save battery life which is limited in WVSNs. To reduce the size of captured images an adaptive block CS technique is proposed and implemented to compress the high volume data images before being transmitted through the wireless channel. The proposed technique divides the image to blocks and adaptively chooses the compression rate for relative blocks containing the target according to the sparsity nature of images. At the receiver side, the compressed image is then reconstructed and target detection and tracking are performed to investigate the effect of CS on the tracking performance. Least mean square adaptive filter is used to predicts target’s next location, an iterative quantized clipped LMS technique is proposed and compared with other variants of LMS and results have shown that it achieved lower error rates than other variants of lMS. The tracking is performed in both indoor and outdoor environments for single/multi targets. Results have shown that with adaptive block compressive sensing (CS) up to 31% measurements of data are required to be transmitted for less sparse images and 15% for more sparse, while preserving the 33dB image quality and the required detection and tracking performance. Adaptive CS resulted in 82% energy saving as compared to transmitting the required image with no C

Exploring information retrieval using image sparse representations:from circuit designs and acquisition processes to specific reconstruction algorithms

New advances in the field of image sensors (especially in CMOS technology) tend to question the conventional methods used to acquire the image. Compressive Sensing (CS) plays a major role in this, especially to unclog the Analog to Digital Converters which are generally representing the bottleneck of this type of sensors. In addition, CS eliminates traditional compression processing stages that are performed by embedded digital signal processors dedicated to this purpose. The interest is twofold because it allows both to consistently reduce the amount of data to be converted but also to suppress digital processing performed out of the sensor chip. For the moment, regarding the use of CS in image sensors, the main route of exploration as well as the intended applications aims at reducing power consumption related to these components (i.e. ADC & DSP represent 99% of the total power consumption). More broadly, the paradigm of CS allows to question or at least to extend the Nyquist-Shannon sampling theory. This thesis shows developments in the field of image sensors demonstrating that is possible to consider alternative applications linked to CS. Indeed, advances are presented in the fields of hyperspectral imaging, super-resolution, high dynamic range, high speed and non-uniform sampling. In particular, three research axes have been deepened, aiming to design proper architectures and acquisition processes with their associated reconstruction techniques taking advantage of image sparse representations. How the on-chip implementation of Compressed Sensing can relax sensor constraints, improving the acquisition characteristics (speed, dynamic range, power consumption) ? How CS can be combined with simple analysis to provide useful image features for high level applications (adding semantic information) and improve the reconstructed image quality at a certain compression ratio ? Finally, how CS can improve physical limitations (i.e. spectral sensitivity and pixel pitch) of imaging systems without a major impact neither on the sensing strategy nor on the optical elements involved ? A CMOS image sensor has been developed and manufactured during this Ph.D. to validate concepts such as the High Dynamic Range - CS. A new design approach was employed resulting in innovative solutions for pixels addressing and conversion to perform specific acquisition in a compressed mode. On the other hand, the principle of adaptive CS combined with the non-uniform sampling has been developed. Possible implementations of this type of acquisition are proposed. Finally, preliminary works are exhibited on the use of Liquid Crystal Devices to allow hyperspectral imaging combined with spatial super-resolution. The conclusion of this study can be summarized as follows: CS must now be considered as a toolbox for defining more easily compromises between the different characteristics of the sensors: integration time, converters speed, dynamic range, resolution and digital processing resources. However, if CS relaxes some material constraints at the sensor level, it is possible that the collected data are difficult to interpret and process at the decoder side, involving massive computational resources compared to so-called conventional techniques. The application field is wide, implying that for a targeted application, an accurate characterization of the constraints concerning both the sensor (encoder), but also the decoder need to be defined

A Methodology for Extracting Human Bodies from Still Images

Monitoring and surveillance of humans is one of the most prominent applications of today and it is expected to be part of many future aspects of our life, for safety reasons, assisted living and many others. Many efforts have been made towards automatic and robust solutions, but the general problem is very challenging and remains still open. In this PhD dissertation we examine the problem from many perspectives. First, we study the performance of a hardware architecture designed for large-scale surveillance systems. Then, we focus on the general problem of human activity recognition, present an extensive survey of methodologies that deal with this subject and propose a maturity metric to evaluate them. One of the numerous and most popular algorithms for image processing found in the field is image segmentation and we propose a blind metric to evaluate their results regarding the activity at local regions. Finally, we propose a fully automatic system for segmenting and extracting human bodies from challenging single images, which is the main contribution of the dissertation. Our methodology is a novel bottom-up approach relying mostly on anthropometric constraints and is facilitated by our research in the fields of face, skin and hands detection. Experimental results and comparison with state-of-the-art methodologies demonstrate the success of our approach

Improving Indoor Security Surveillance by Fusing Data from BIM, UWB and Video

Indoor physical security, as a perpetual and multi-layered phenomenon, is a time-intensive and labor-consuming task. Various technologies have been leveraged to develop automatic access control, intrusion detection, or video monitoring systems. Video surveillance has been significantly enhanced by the advent of Pan-Tilt-Zoom (PTZ) cameras and advanced video processing, which together enable effective monitoring and recording. The development of ubiquitous object identification and tracking technologies provides the opportunity to accomplish automatic access control and tracking. Intrusion detection has also become possible through deploying networks of motion sensors for alerting about abnormal behaviors. However, each of the above-mentioned technologies has its own limitations. This thesis presents a fully automated indoor security solution that leverages an Ultra-wideband (UWB) Real-Time Locating System (RTLS), PTZ surveillance cameras and a Building Information Model (BIM) as three sources of environmental data. Providing authorized persons with UWB tags, unauthorized intruders are distinguished as the mismatch observed between the detected tag owners and the persons detected in the video, and intrusion alert is generated. PTZ cameras allow for wide-area monitoring and motion-based recording. Furthermore, the BIM is used for space modeling and mapping the locations of intruders in the building. Fusing UWB tracking, video and spatial data can automate the entire security procedure from access control to intrusion alerting and behavior monitoring. Other benefits of the proposed method include more complex query processing and interoperability with other BIM-based solutions. A prototype system is implemented that demonstrates the feasibility of the proposed method

Statistical and Graph-Based Signal Processing: Fundamental Results and Application to Cardiac Electrophysiology

The goal of cardiac electrophysiology is to obtain information about the mechanism, function, and performance of the electrical activities of the heart, the identification of deviation from normal pattern and the design of treatments. Offering a better insight into cardiac arrhythmias comprehension and management, signal processing can help the physician to enhance the treatment strategies, in particular in case of atrial fibrillation (AF), a very common atrial arrhythmia which is associated to significant morbidities, such as increased risk of mortality, heart failure, and thromboembolic events. Catheter ablation of AF is a therapeutic technique which uses radiofrequency energy to destroy atrial tissue involved in the arrhythmia sustenance, typically aiming at the electrical disconnection of the of the pulmonary veins triggers. However, recurrence rate is still very high, showing that the very complex and heterogeneous nature of AF still represents a challenging problem. Leveraging the tools of non-stationary and statistical signal processing, the first part of our work has a twofold focus: firstly, we compare the performance of two different ablation technologies, based on contact force sensing or remote magnetic controlled, using signal-based criteria as surrogates for lesion assessment. Furthermore, we investigate the role of ablation parameters in lesion formation using the late-gadolinium enhanced magnetic resonance imaging. Secondly, we hypothesized that in human atria the frequency content of the bipolar signal is directly related to the local conduction velocity (CV), a key parameter characterizing the substrate abnormality and influencing atrial arrhythmias. Comparing the degree of spectral compression among signals recorded at different points of the endocardial surface in response to decreasing pacing rate, our experimental data demonstrate a significant correlation between CV and the corresponding spectral centroids. However, complex spatio-temporal propagation pattern characterizing AF spurred the need for new signals acquisition and processing methods. Multi-electrode catheters allow whole-chamber panoramic mapping of electrical activity but produce an amount of data which need to be preprocessed and analyzed to provide clinically relevant support to the physician. Graph signal processing has shown its potential on a variety of applications involving high-dimensional data on irregular domains and complex network. Nevertheless, though state-of-the-art graph-based methods have been successful for many tasks, so far they predominantly ignore the time-dimension of data. To address this shortcoming, in the second part of this dissertation, we put forth a Time-Vertex Signal Processing Framework, as a particular case of the multi-dimensional graph signal processing. Linking together the time-domain signal processing techniques with the tools of GSP, the Time-Vertex Signal Processing facilitates the analysis of graph structured data which also evolve in time. We motivate our framework leveraging the notion of partial differential equations on graphs. We introduce joint operators, such as time-vertex localization and we present a novel approach to significantly improve the accuracy of fast joint filtering. We also illustrate how to build time-vertex dictionaries, providing conditions for efficient invertibility and examples of constructions. The experimental results on a variety of datasets suggest that the proposed tools can bring significant benefits in various signal processing and learning tasks involving time-series on graphs. We close the gap between the two parts illustrating the application of graph and time-vertex signal processing to the challenging case of multi-channels intracardiac signals