Object detection, recognition and re-identification in video footage

There has been a significant number of security concerns in recent times; as a result, security cameras have been installed to monitor activities and to prevent crimes in most public places. These analysis are done either through video analytic or forensic analysis operations on human observations. To this end, within the research context of this thesis, a proactive machine vision based military recognition system has been developed to help monitor activities in the military environment. The proposed object detection, recognition and re-identification systems have been presented in this thesis. A novel technique for military personnel recognition is presented in this thesis. Initially the detected camouflaged personnel are segmented using a grabcut segmentation algorithm. Since in general a camouflaged personnel's uniform appears to be similar both at the top and the bottom of the body, an image patch is initially extracted from the segmented foreground image and used as the region of interest. Subsequently the colour and texture features are extracted from each patch and used for classification. A second approach for personnel recognition is proposed through the recognition of the badge on the cap of a military person. A feature matching metric based on the extracted Speed Up Robust Features (SURF) from the badge on a personnel's cap enabled the recognition of the personnel's arm of service. A state-of-the-art technique for recognising vehicle types irrespective of their view angle is also presented in this thesis. Vehicles are initially detected and segmented using a Gaussian Mixture Model (GMM) based foreground/background segmentation algorithm. A Canny Edge Detection (CED) stage, followed by morphological operations are used as pre-processing stage to help enhance foreground vehicular object detection and segmentation. Subsequently, Region, Histogram Oriented Gradient (HOG) and Local Binary Pattern (LBP) features are extracted from the refined foreground vehicle object and used as features for vehicle type recognition. Two different datasets with variant views of front/rear and angle are used and combined for testing the proposed technique. For night-time video analytics and forensics, the thesis presents a novel approach to pedestrian detection and vehicle type recognition. A novel feature acquisition technique named, CENTROG, is proposed for pedestrian detection and vehicle type recognition in this thesis. Thermal images containing pedestrians and vehicular objects are used to analyse the performance of the proposed algorithms. The video is initially segmented using a GMM based foreground object segmentation algorithm. A CED based pre-processing step is used to enhance segmentation accuracy prior using Census Transforms for initial feature extraction. HOG features are then extracted from the Census transformed images and used for detection and recognition respectively of human and vehicular objects in thermal images. Finally, a novel technique for people re-identification is proposed in this thesis based on using low-level colour features and mid-level attributes. The low-level colour histogram bin values were normalised to 0 and 1. A publicly available dataset (VIPeR) and a self constructed dataset have been used in the experiments conducted with 7 clothing attributes and low-level colour histogram features. These 7 attributes are detected using features extracted from 5 different regions of a detected human object using an SVM classifier. The low-level colour features were extracted from the regions of a detected human object. These 5 regions are obtained by human object segmentation and subsequent body part sub-division. People are re-identified by computing the Euclidean distance between a probe and the gallery image sets. The experiments conducted using SVM classifier and Euclidean distance has proven that the proposed techniques attained all of the aforementioned goals. The colour and texture features proposed for camouflage military personnel recognition surpasses the state-of-the-art methods. Similarly, experiments prove that combining features performed best when recognising vehicles in different views subsequent to initial training based on multi-views. In the same vein, the proposed CENTROG technique performed better than the state-of-the-art CENTRIST technique for both pedestrian detection and vehicle type recognition at night-time using thermal images. Finally, we show that the proposed 7 mid-level attributes and the low-level features results in improved performance accuracy for people re-identification

FACE RECOGNITION AND VERIFICATION IN UNCONSTRAINED ENVIRIONMENTS

Face recognition has been a long standing problem in computer vision. General face recognition is challenging because of large appearance variability due to factors including pose, ambient lighting, expression, size of the face, age, and distance from the camera, etc. There are very accurate techniques to perform face recognition in controlled environments, especially when large numbers of samples are available for each face (individual). However, face identification under uncontrolled( unconstrained) environments or with limited training data is still an unsolved problem. There are two face recognition tasks: face identification (who is who in a probe face set, given a gallery face set) and face verification (same or not, given two faces). In this work, we study both face identification and verification in unconstrained environments. Firstly, we propose a face verification framework that combines Partial Least Squares (PLS) and the One-Shot similarity model[1]. The idea is to describe a face with a large feature set combining shape, texture and color information. PLS regression is applied to perform multi-channel feature weighting on this large feature set. Finally the PLS regression is used to compute the similarity score of an image pair by One-Shot learning (using a fixed negative set). Secondly, we study face identification with image sets, where the gallery and probe are sets of face images of an individual. We model a face set by its covariance matrix (COV) which is a natural 2nd-order statistic of a sample set.By exploring an efficient metric for the SPD matrices, i.e., Log-Euclidean Distance (LED), we derive a kernel function that explicitly maps the covariance matrix from the Riemannian manifold to Euclidean space. Then, discriminative learning is performed on the COV manifold: the learning aims to maximize the between-class COV distance and minimize the within-class COV distance. Sparse representation and dictionary learning have been widely used in face recognition, especially when large numbers of samples are available for each face (individual). Sparse coding is promising since it provides a more stable and discriminative face representation. In the last part of our work, we explore sparse coding and dictionary learning for face verification application. More specifically, in one approach, we apply sparse representations to face verification in two ways via a fix reference set as dictionary. In the other approach, we propose a dictionary learning framework with explicit pairwise constraints, which unifies the discriminative dictionary learning for pair matching (face verification) and classification (face recognition) problems