Enhanced Automated Framework For Cattle Tracking And Classification

Employing computer vision-based methods in monitoring individual cows has become what researchers are striving for. Computer vision-based methods could be used to monitor each individual cows. The accuracy of the existing methods and frameworks is below expectation in handling these tasks. Moreover, they can still be improved to achieve better and more accurate results. The goal of this research is to provide a framework for better cattle tracking and classification systems. An enhanced object tracking algorithm (PFtmM) that integrates enhanced particle filter algorithm (PFtm) with mean-shift tracker (M) is proposed and deployed as first step to address the problems arise due to occurrence of occlusion and non-linear movement of cow objects in video. The integration of particle filter with mean-shift tracker considers the following techniques: (1) temporary memory for keeping tracks of occluded cow objects; (2) supplementing each algorithm’s weakness by the strength of the other for tracking non-linear movement. Strength of particle filter (PF) is its non-linearity property which it uses to track object’s non-linear movement but, with high computational time and search range as its weakness. Temporary memory (tm) strength is its ability to track full occlusion with reduced computational time and search range. Mean-shift strength is its sensitivity to object’s movement and colour distribution by using similarity function but, with inability to track object’s non-linear movement and full occlusion as its weakness

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Real-time tissue viability assessment using near-infrared light

Despite significant advances in medical imaging technologies, there currently exist no tools to effectively assist healthcare professionals during surgical procedures. In turn, procedures remain subjective and dependent on experience, resulting in avoidable failure and significant quality of care disparities across hospitals. Optical techniques are gaining popularity in clinical research because they are low cost, non-invasive, portable, and can retrieve both fluorescence and endogenous contrast information, providing physiological information relative to perfusion, oxygenation, metabolism, hydration, and sub-cellular content. Near-infrared (NIR) light is especially well suited for biological tissue and does not cause tissue damage from ionizing radiation or heat. My dissertation has been focused on developing rapid imaging techniques for mapping endogenous tissue constituents to aid surgical guidance. These techniques allow, for the first time, video-rate quantitative acquisition over a large field of view (> 100 cm2) in widefield and endoscopic implementations. The optical system analysis has been focused on the spatial-frequency domain for its ease of quantitative measurements over large fields of view and for its recent development in real-time acquisition, single snapshot of optical properties (SSOP) imaging. Using these methods, this dissertation provides novel improvements and implementations to SSOP, including both widefield and endoscopic instrumentations capable of video-rate acquisition of optical properties and sample surface profile maps. In turn, these measures generate profile-corrected maps of hemoglobin concentration that are highly beneficial for perfusion and overall tissue viability. Also utilizing optical property maps, a novel technique for quantitative fluorescence imaging was also demonstrated, showing large improvement over standard and ratiometric methods. To enable real-time feedback, rapid processing algorithms were designed using lookup tables that provide a 100x improvement in processing speed. Finally, these techniques were demonstrated in vivo to investigate their ability for early detection of tissue failure due to ischemia. Both pre-clinical studies show endogenous contrast imaging can provide early measures of future tissue viability. The goal of this work has been to provide the foundation for real-time imaging systems that provide tissue constituent quantification for tissue viability assessments.2018-01-09T00:00:00

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

Enhancing Real-time Embedded Image Processing Robustness on Reconfigurable Devices for Critical Applications

Nowadays, image processing is increasingly used in several application fields, such as biomedical, aerospace, or automotive. Within these fields, image processing is used to serve both non-critical and critical tasks. As example, in automotive, cameras are becoming key sensors in increasing car safety, driving assistance and driving comfort. They have been employed for infotainment (non-critical), as well as for some driver assistance tasks (critical), such as Forward Collision Avoidance, Intelligent Speed Control, or Pedestrian Detection. The complexity of these algorithms brings a challenge in real-time image processing systems, requiring high computing capacity, usually not available in processors for embedded systems. Hardware acceleration is therefore crucial, and devices such as Field Programmable Gate Arrays (FPGAs) best fit the growing demand of computational capabilities. These devices can assist embedded processors by significantly speeding-up computationally intensive software algorithms. Moreover, critical applications introduce strict requirements not only from the real-time constraints, but also from the device reliability and algorithm robustness points of view. Technology scaling is highlighting reliability problems related to aging phenomena, and to the increasing sensitivity of digital devices to external radiation events that can cause transient or even permanent faults. These faults can lead to wrong information processed or, in the worst case, to a dangerous system failure. In this context, the reconfigurable nature of FPGA devices can be exploited to increase the system reliability and robustness by leveraging Dynamic Partial Reconfiguration features. The research work presented in this thesis focuses on the development of techniques for implementing efficient and robust real-time embedded image processing hardware accelerators and systems for mission-critical applications. Three main challenges have been faced and will be discussed, along with proposed solutions, throughout the thesis: (i) achieving real-time performances, (ii) enhancing algorithm robustness, and (iii) increasing overall system's dependability. In order to ensure real-time performances, efficient FPGA-based hardware accelerators implementing selected image processing algorithms have been developed. Functionalities offered by the target technology, and algorithm's characteristics have been constantly taken into account while designing such accelerators, in order to efficiently tailor algorithm's operations to available hardware resources. On the other hand, the key idea for increasing image processing algorithms' robustness is to introduce self-adaptivity features at algorithm level, in order to maintain constant, or improve, the quality of results for a wide range of input conditions, that are not always fully predictable at design-time (e.g., noise level variations). This has been accomplished by measuring at run-time some characteristics of the input images, and then tuning the algorithm parameters based on such estimations. Dynamic reconfiguration features of modern reconfigurable FPGA have been extensively exploited in order to integrate run-time adaptivity into the designed hardware accelerators. Tools and methodologies have been also developed in order to increase the overall system dependability during reconfiguration processes, thus providing safe run-time adaptation mechanisms. In addition, taking into account the target technology and the environments in which the developed hardware accelerators and systems may be employed, dependability issues have been analyzed, leading to the development of a platform for quickly assessing the reliability and characterizing the behavior of hardware accelerators implemented on reconfigurable FPGAs when they are affected by such faults

Automated 3D model generation for urban environments [online]

Abstract In this thesis, we present a fast approach to automated generation of textured 3D city models with both high details at ground level and complete coverage for birds-eye view. A ground-based facade model is acquired by driving a vehicle equipped with two 2D laser scanners and a digital camera under normal traffic conditions on public roads. One scanner is mounted horizontally and is used to determine the approximate component of relative motion along the movement of the acquisition vehicle via scan matching; the obtained relative motion estimates are concatenated to form an initial path. Assuming that features such as buildings are visible from both ground-based and airborne view, this initial path is globally corrected by Monte-Carlo Localization techniques using an aerial photograph or a Digital Surface Model as a global map. The second scanner is mounted vertically and is used to capture the 3D shape of the building facades. Applying a series of automated processing steps, a texture-mapped 3D facade model is reconstructed from the vertical laser scans and the camera images. In order to obtain an airborne model containing the roof and terrain shape complementary to the facade model, a Digital Surface Model is created from airborne laser scans, then triangulated, and finally texturemapped with aerial imagery. Finally, the facade model and the airborne model are fused to one single model usable for both walk- and fly-thrus. The developed algorithms are evaluated on a large data set acquired in downtown Berkeley, and the results are shown and discussed

Deep Feature Learning and Adaptation for Computer Vision

We are living in times when a revolution of deep learning is taking place. In general, deep learning models have a backbone that extracts features from the input data followed by task-specific layers, e.g. for classification. This dissertation proposes various deep feature extraction and adaptation methods to improve task-specific learning, such as visual re-identification, tracking, and domain adaptation. The vehicle re-identification (VRID) task requires identifying a given vehicle among a set of vehicles under variations in viewpoint, illumination, partial occlusion, and background clutter. We propose a novel local graph aggregation module for feature extraction to improve VRID performance. We also utilize a class-balanced loss to compensate for the unbalanced class distribution in the training dataset. Overall, our framework achieves state-of-the-art (SOTA) performance in multiple VRID benchmarks. We further extend our VRID method for visual object tracking under occlusion conditions. We motivate visual object tracking from aerial platforms by conducting a benchmarking of tracking methods on aerial datasets. Our study reveals that the current techniques have limited capabilities to re-identify objects when fully occluded or out of view. The Siamese network based trackers perform well compared to others in overall tracking performance. We utilize our VRID work in visual object tracking and propose Siam-ReID, a novel tracking method using a Siamese network and VRID technique. In another approach, we propose SiamGauss, a novel Siamese network with a Gaussian Head for improved confuser suppression and real time performance. Our approach achieves SOTA performance on aerial visual object tracking datasets. A related area of research is developing deep learning based domain adaptation techniques. We propose continual unsupervised domain adaptation, a novel paradigm for domain adaptation in data constrained environments. We show that existing works fail to generalize when the target domain data are acquired in small batches. We propose to use a buffer to store samples that are previously seen by the network and a novel loss function to improve the performance of continual domain adaptation. We further extend our continual unsupervised domain adaptation research for gradually varying domains. Our method outperforms several SOTA methods even though they have the entire domain data available during adaptation

Bayesian-based techniques for tracking multiple humans in an enclosed environment

This thesis deals with the problem of online visual tracking of multiple humans in an enclosed environment. The focus is to develop techniques to deal with the challenges of varying number of targets, inter-target occlusions and interactions when every target gives rise to multiple measurements (pixels) in every video frame. This thesis contains three different contributions to the research in multi-target tracking. Firstly, a multiple target tracking algorithm is proposed which focuses on mitigating the inter-target occlusion problem during complex interactions. This is achieved with the help of a particle filter, multiple video cues and a new interaction model. A Markov chain Monte Carlo particle filter (MCMC-PF) is used along with a new interaction model which helps in modeling interactions of multiple targets. This helps to overcome tracking failures due to occlusions. A new weighted Markov chain Monte Carlo (WMCMC) sampling technique is also proposed which assists in achieving a reduced tracking error. Although effective, to accommodate multiple measurements (pixels) produced by every target, this technique aggregates measurements into features which results in information loss. In the second contribution, a novel variational Bayesian clustering-based multi-target tracking framework is proposed which can associate multiple measurements to every target without aggregating them into features. It copes with complex inter-target occlusions by maintaining the identity of targets during their close physical interactions and handles efficiently a time-varying number of targets. The proposed multi-target tracking framework consists of background subtraction, clustering, data association and particle filtering. A variational Bayesian clustering technique groups the extracted foreground measurements while an improved feature based joint probabilistic data association filter (JPDAF) is developed to associate clusters of measurements to every target. The data association information is used within the particle filter to track multiple targets. The clustering results are further utilised to estimate the number of targets. The proposed technique improves the tracking accuracy. However, the proposed features based JPDAF technique results in an exponential growth of computational complexity of the overall framework with increase in number of targets. In the final work, a novel data association technique for multi-target tracking is proposed which more efficiently assigns multiple measurements to every target, with a reduced computational complexity. A belief propagation (BP) based cluster to target association method is proposed which exploits the inter-cluster dependency information. Both location and features of clusters are used to re-identify the targets when they emerge from occlusions. The proposed techniques are evaluated on benchmark data sets and their performance is compared with state-of-the-art techniques by using, quantitative and global performance measures