Features for matching people in different views

There have been significant advances in the computer vision field during the last decade. During this period, many methods have been developed that have been successful in solving challenging problems including Face Detection, Object Recognition and 3D Scene Reconstruction. The solutions developed by computer vision researchers have been widely adopted and used in many real-life applications such as those faced in the medical and security industry. Among the different branches of computer vision, Object Recognition has been an area that has advanced rapidly in recent years. The successful introduction of approaches such as feature extraction and description has been an important factor in the growth of this area. In recent years, researchers have attempted to use these approaches and apply them to other problems such as Content Based Image Retrieval and Tracking. In this work, we present a novel system that finds correspondences between people seen in different images. Unlike other approaches that rely on a video stream to track the movement of people between images, here we present a feature-based approach where we locate a target’s new location in an image, based only on its visual appearance. Our proposed system comprises three steps. In the first step, a set of features is extracted from the target’s appearance. A novel algorithm is developed that allows extraction of features from a target that is particularly suitable to the modelling task. In the second step, each feature is characterised using a combined colour and texture descriptor. Inclusion of information relating to both colour and texture of a feature add to the descriptor’s distinctiveness. Finally, the target’s appearance and pose is modelled as a collection of such features and descriptors. This collection is then used as a template that allows us to search for a similar combination of features in other images that correspond to the target’s new location. We have demonstrated the effectiveness of our system in locating a target’s new position in an image, despite differences in viewpoint, scale or elapsed time between the images. The characterisation of a target as a collection of features also allows our system to robustly deal with the partial occlusion of the target

Local Binary Pattern based algorithms for the discrimination and detection of crops and weeds with similar morphologies

In cultivated agricultural fields, weeds are unwanted species that compete with the crop plants for nutrients, water, sunlight and soil, thus constraining their growth. Applying new real-time weed detection and spraying technologies to agriculture would enhance current farming practices, leading to higher crop yields and lower production costs. Various weed detection methods have been developed for Site-Specific Weed Management (SSWM) aimed at maximising the crop yield through efficient control of weeds. Blanket application of herbicide chemicals is currently the most popular weed eradication practice in weed management and weed invasion. However, the excessive use of herbicides has a detrimental impact on the human health, economy and environment. Before weeds are resistant to herbicides and respond better to weed control strategies, it is necessary to control them in the fallow, pre-sowing, early post-emergent and in pasture phases. Moreover, the development of herbicide resistance in weeds is the driving force for inventing precision and automation weed treatments. Various weed detection techniques have been developed to identify weed species in crop fields, aimed at improving the crop quality, reducing herbicide and water usage and minimising environmental impacts. In this thesis, Local Binary Pattern (LBP)-based algorithms are developed and tested experimentally, which are based on extracting dominant plant features from camera images to precisely detecting weeds from crops in real time. Based on the efficient computation and robustness of the first LBP method, an improved LBP-based method is developed based on using three different LBP operators for plant feature extraction in conjunction with a Support Vector Machine (SVM) method for multiclass plant classification. A 24,000-image dataset, collected using a testing facility under simulated field conditions (Testbed system), is used for algorithm training, validation and testing. The dataset, which is published online under the name “bccr-segset”, consists of four subclasses: background, Canola (Brassica napus), Corn (Zea mays), and Wild radish (Raphanus raphanistrum). In addition, the dataset comprises plant images collected at four crop growth stages, for each subclass. The computer-controlled Testbed is designed to rapidly label plant images and generate the “bccr-segset” dataset. Experimental results show that the classification accuracy of the improved LBP-based algorithm is 91.85%, for the four classes. Due to the similarity of the morphologies of the canola (crop) and wild radish (weed) leaves, the conventional LBP-based method has limited ability to discriminate broadleaf crops from weeds. To overcome this limitation and complex field conditions (illumination variation, poses, viewpoints, and occlusions), a novel LBP-based method (denoted k-FLBPCM) is developed to enhance the classification accuracy of crops and weeds with similar morphologies. Our contributions include (i) the use of opening and closing morphological operators in pre-processing of plant images, (ii) the development of the k-FLBPCM method by combining two methods, namely, the filtered local binary pattern (LBP) method and the contour-based masking method with a coefficient k, and (iii) the optimal use of SVM with the radial basis function (RBF) kernel to precisely identify broadleaf plants based on their distinctive features. The high performance of this k-FLBPCM method is demonstrated by experimentally attaining up to 98.63% classification accuracy at four different growth stages for all classes of the “bccr-segset” dataset. To evaluate performance of the k-FLBPCM algorithm in real-time, a comparison analysis between our novel method (k-FLBPCM) and deep convolutional neural networks (DCNNs) is conducted on morphologically similar crops and weeds. Various DCNN models, namely VGG-16, VGG-19, ResNet50 and InceptionV3, are optimised, by fine-tuning their hyper-parameters, and tested. Based on the experimental results on the “bccr-segset” dataset collected from the laboratory and the “fieldtrip_can_weeds” dataset collected from the field under practical environments, the classification accuracies of the DCNN models and the k-FLBPCM method are almost similar. Another experiment is conducted by training the algorithms with plant images obtained at mature stages and testing them at early stages. In this case, the new k-FLBPCM method outperformed the state-of-the-art CNN models in identifying small leaf shapes of canola-radish (crop-weed) at early growth stages, with an order of magnitude lower error rates in comparison with DCNN models. Furthermore, the execution time of the k-FLBPCM method during the training and test phases was faster than the DCNN counterparts, with an identification time difference of approximately 0.224ms per image for the laboratory dataset and 0.346ms per image for the field dataset. These results demonstrate the ability of the k-FLBPCM method to rapidly detect weeds from crops of similar appearance in real time with less data, and generalize to different size plants better than the CNN-based methods

Video foreground extraction for mobile camera platforms

Foreground object detection is a fundamental task in computer vision with many applications in areas such as object tracking, event identification, and behavior analysis. Most conventional foreground object detection methods work only in a stable illumination environments using fixed cameras. In real-world applications, however, it is often the case that the algorithm needs to operate under the following challenging conditions: drastic lighting changes, object shape complexity, moving cameras, low frame capture rates, and low resolution images. This thesis presents four novel approaches for foreground object detection on real-world datasets using cameras deployed on moving vehicles.The first problem addresses passenger detection and tracking tasks for public transport buses investigating the problem of changing illumination conditions and low frame capture rates. Our approach integrates a stable SIFT (Scale Invariant Feature Transform) background seat modelling method with a human shape model into a weighted Bayesian framework to detect passengers. To deal with the problem of tracking multiple targets, we employ the Reversible Jump Monte Carlo Markov Chain tracking algorithm. Using the SVM classifier, the appearance transformation models capture changes in the appearance of the foreground objects across two consecutives frames under low frame rate conditions. In the second problem, we present a system for pedestrian detection involving scenes captured by a mobile bus surveillance system. It integrates scene localization, foreground-background separation, and pedestrian detection modules into a unified detection framework. The scene localization module performs a two stage clustering of the video data.In the first stage, SIFT Homography is applied to cluster frames in terms of their structural similarity, and the second stage further clusters these aligned frames according to consistency in illumination. This produces clusters of images that are differential in viewpoint and lighting. A kernel density estimation (KDE) technique for colour and gradient is then used to construct background models for each image cluster, which is further used to detect candidate foreground pixels. Finally, using a hierarchical template matching approach, pedestrians can be detected.In addition to the second problem, we present three direct pedestrian detection methods that extend the HOG (Histogram of Oriented Gradient) techniques (Dalal and Triggs, 2005) and provide a comparative evaluation of these approaches. The three approaches include: a) a new histogram feature, that is formed by the weighted sum of both the gradient magnitude and the filter responses from a set of elongated Gaussian filters (Leung and Malik, 2001) corresponding to the quantised orientation, which we refer to as the Histogram of Oriented Gradient Banks (HOGB) approach; b) the codebook based HOG feature with branch-and-bound (efficient subwindow search) algorithm (Lampert et al., 2008) and; c) the codebook based HOGB approach.In the third problem, a unified framework that combines 3D and 2D background modelling is proposed to detect scene changes using a camera mounted on a moving vehicle. The 3D scene is first reconstructed from a set of videos taken at different times. The 3D background modelling identifies inconsistent scene structures as foreground objects. For the 2D approach, foreground objects are detected using the spatio-temporal MRF algorithm. Finally, the 3D and 2D results are combined using morphological operations.The significance of these research is that it provides basic frameworks for automatic large-scale mobile surveillance applications and facilitates many higher-level applications such as object tracking and behaviour analysis

Effective plant discrimination based on the combination of local binary pattern operators and multiclass support vector machine methods

Accurate crop and weed discrimination plays a critical role in addressing the challenges of weed management in agriculture. The use of herbicides is currently the most common approach to weed control. However, herbicide resistant plants have long been recognised as a major concern due to the excessive use of herbicides. Effective weed detection techniques can reduce the cost of weed management and improve crop quality and yield. A computationally efficient and robust plant classification algorithm is developed and applied to the classification of three crops: Brassica napus (canola), Zea mays (maize/corn), and radish. The developed algorithm is based on the combination of Local Binary Pattern (LBP) operators, for the extraction of crop leaf textural features and Support vector machine (SVM) method, for multiclass plant classification. This paper presents the first investigation of the accuracy of the combined LBP algorithms, trained using a large dataset of canola, radish and barley leaf images captured by a testing facility under simulated field conditions. The dataset has four subclasses, background, canola, corn, and radish, with 24,000 images used for training and 6000 images, for validation. The dataset is referred herein as “bccr-segset” and published online. In each subclass, plant images are collected at four crop growth stages. Experimentally, the algorithm demonstrates plant classification accuracy as high as 91.85%, for the four classes. © 2018 China Agricultural Universit

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Data Driven Multispectral Image Registration Framework

Multispectral imaging is widely used in remote sensing applications from UAVs and ground-based platforms. Multispectral cameras often use a physically different camera for each wavelength causing misalignment in the images for different imaging bands. This misalignment must be corrected prior to concurrent multi-band image analysis. The traditional approach for multispectral image registration process is to select a target channel and register all other image channels to the target. There is no objective evidence-based method to select a target channel. The possibility of registration to some intermediate channel before registering to the target is not usually considered, but could be beneficial if there is no target channel for which direct registration performs well for every other channel. In this paper, we propose an automatic data-driven multispectral image registration framework that determines a target channel, and possible intermediate registration steps based on the assumptions that 1) some reasonable minimum number of control-points correspondences between two channels is needed to ensure a low-error registration; 2) a greater number of such correspondences generally results in higher registration performance. Our prototype is tested on five multispectral datasets captured with UAV-mounted multispectral cameras. The output of the prototype is a registration scheme in the form of a directed acyclic graph (actually a tree) that represents the target channel and the process to register other image channels. The resulting registration schemes had more control point correspondences on average than the traditional register-all-to-one-targetchannel approach. Data-driven registration scheme consistently showed low back-projection error across all the image channel pairs in most of the experiments. Our data-driven framework has generated registration schemes with the best control point extraction algorithm for each image channel pair and registering images in a data-driven approach. The data-driven image registration framework is dataset independent, and it performs on datasets with any number of image channels. With the growing need of remote sensing and the lack of a proper evidence-based method to register multispectral image channels, a data-driven registration framework is an essential tool in the field of image registration and multispectral imaging