Human object annotation for surveillance video forensics

A system that can automatically annotate surveillance video in a manner useful for locating a person with a given description of clothing is presented. Each human is annotated based on two appearance features: primary colors of clothes and the presence of text/logos on clothes. The annotation occurs after a robust foreground extraction stage employing a modified Gaussian mixture model-based approach. The proposed pipeline consists of a preprocessing stage where color appearance of an image is improved using a color constancy algorithm. In order to annotate color information for human clothes, we use the color histogram feature in HSV space and find local maxima to extract dominant colors for different parts of a segmented human object. To detect text/logos on clothes, we begin with the extraction of connected components of enhanced horizontal, vertical, and diagonal edges in the frames. These candidate regions are classified as text or nontext on the basis of their local energy-based shape histogram features. Further, to detect humans, a novel technique has been proposed that uses contourlet transform-based local binary pattern (CLBP) features. In the proposed method, we extract the uniform direction invariant LBP feature descriptor for contourlet transformed high-pass subimages from vertical and diagonal directional bands. In the final stage, extracted CLBP descriptors are classified by a trained support vector machine. Experimental results illustrate the superiority of our method on large-scale surveillance video data

Use of Coherent Point Drift in computer vision applications

This thesis presents the novel use of Coherent Point Drift in improving the robustness of a number of computer vision applications. CPD approach includes two methods for registering two images - rigid and non-rigid point set approaches which are based on the transformation model used. The key characteristic of a rigid transformation is that the distance between points is preserved, which means it can be used in the presence of translation, rotation, and scaling. Non-rigid transformations - or affine transforms - provide the opportunity of registering under non-uniform scaling and skew. The idea is to move one point set coherently to align with the second point set. The CPD method finds both the non-rigid transformation and the correspondence distance between two point sets at the same time without having to use a-priori declaration of the transformation model used. The first part of this thesis is focused on speaker identification in video conferencing. A real-time, audio-coupled video based approach is presented, which focuses more on the video analysis side, rather than the audio analysis that is known to be prone to errors. CPD is effectively utilised for lip movement detection and a temporal face detection approach is used to minimise false positives if face detection algorithm fails to perform. The second part of the thesis is focused on multi-exposure and multi-focus image fusion with compensation for camera shake. Scale Invariant Feature Transforms (SIFT) are first used to detect keypoints in images being fused. Subsequently this point set is reduced to remove outliers, using RANSAC (RANdom Sample Consensus) and finally the point sets are registered using CPD with non-rigid transformations. The registered images are then fused with a Contourlet based image fusion algorithm that makes use of a novel alpha blending and filtering technique to minimise artefacts. The thesis evaluates the performance of the algorithm in comparison to a number of state-of-the-art approaches, including the key commercial products available in the market at present, showing significantly improved subjective quality in the fused images. The final part of the thesis presents a novel approach to Vehicle Make & Model Recognition in CCTV video footage. CPD is used to effectively remove skew of vehicles detected as CCTV cameras are not specifically configured for the VMMR task and may capture vehicles at different approaching angles. A LESH (Local Energy Shape Histogram) feature based approach is used for vehicle make and model recognition with the novelty that temporal processing is used to improve reliability. A number of further algorithms are used to maximise the reliability of the final outcome. Experimental results are provided to prove that the proposed system demonstrates an accuracy in excess of 95% when tested on real CCTV footage with no prior camera calibration

Video content analysis for intelligent forensics

The networks of surveillance cameras installed in public places and private territories continuously record video data with the aim of detecting and preventing unlawful activities. This enhances the importance of video content analysis applications, either for real time (i.e. analytic) or post-event (i.e. forensic) analysis. In this thesis, the primary focus is on four key aspects of video content analysis, namely; 1. Moving object detection and recognition, 2. Correction of colours in the video frames and recognition of colours of moving objects, 3. Make and model recognition of vehicles and identification of their type, 4. Detection and recognition of text information in outdoor scenes. To address the first issue, a framework is presented in the first part of the thesis that efficiently detects and recognizes moving objects in videos. The framework targets the problem of object detection in the presence of complex background. The object detection part of the framework relies on background modelling technique and a novel post processing step where the contours of the foreground regions (i.e. moving object) are refined by the classification of edge segments as belonging either to the background or to the foreground region. Further, a novel feature descriptor is devised for the classification of moving objects into humans, vehicles and background. The proposed feature descriptor captures the texture information present in the silhouette of foreground objects. To address the second issue, a framework for the correction and recognition of true colours of objects in videos is presented with novel noise reduction, colour enhancement and colour recognition stages. The colour recognition stage makes use of temporal information to reliably recognize the true colours of moving objects in multiple frames. The proposed framework is specifically designed to perform robustly on videos that have poor quality because of surrounding illumination, camera sensor imperfection and artefacts due to high compression. In the third part of the thesis, a framework for vehicle make and model recognition and type identification is presented. As a part of this work, a novel feature representation technique for distinctive representation of vehicle images has emerged. The feature representation technique uses dense feature description and mid-level feature encoding scheme to capture the texture in the frontal view of the vehicles. The proposed method is insensitive to minor in-plane rotation and skew within the image. The capability of the proposed framework can be enhanced to any number of vehicle classes without re-training. Another important contribution of this work is the publication of a comprehensive up to date dataset of vehicle images to support future research in this domain. The problem of text detection and recognition in images is addressed in the last part of the thesis. A novel technique is proposed that exploits the colour information in the image for the identification of text regions. Apart from detection, the colour information is also used to segment characters from the words. The recognition of identified characters is performed using shape features and supervised learning. Finally, a lexicon based alignment procedure is adopted to finalize the recognition of strings present in word images. Extensive experiments have been conducted on benchmark datasets to analyse the performance of proposed algorithms. The results show that the proposed moving object detection and recognition technique superseded well-know baseline techniques. The proposed framework for the correction and recognition of object colours in video frames achieved all the aforementioned goals. The performance analysis of the vehicle make and model recognition framework on multiple datasets has shown the strength and reliability of the technique when used within various scenarios. Finally, the experimental results for the text detection and recognition framework on benchmark datasets have revealed the potential of the proposed scheme for accurate detection and recognition of text in the wild

Automatic vehicle detection and tracking in aerial video

This thesis is concerned with the challenging tasks of automatic and real-time vehicle detection and tracking from aerial video. The aim of this thesis is to build an automatic system that can accurately localise any vehicles that appear in aerial video frames and track the target vehicles with trackers. Vehicle detection and tracking have many applications and this has been an active area of research during recent years; however, it is still a challenge to deal with certain realistic environments. This thesis develops vehicle detection and tracking algorithms which enhance the robustness of detection and tracking beyond the existing approaches. The basis of the vehicle detection system proposed in this thesis has different object categorisation approaches, with colour and texture features in both point and area template forms. The thesis also proposes a novel Self-Learning Tracking and Detection approach, which is an extension to the existing Tracking Learning Detection (TLD) algorithm. There are a number of challenges in vehicle detection and tracking. The most difficult challenge of detection is distinguishing and clustering the target vehicle from the background objects and noises. Under certain conditions, the images captured from Unmanned Aerial Vehicles (UAVs) are also blurred; for example, turbulence may make the vehicle shake during flight. This thesis tackles these challenges by applying integrated multiple feature descriptors for real-time processing. In this thesis, three vehicle detection approaches are proposed: the HSV-GLCM feature approach, the ISM-SIFT feature approach and the FAST-HoG approach. The general vehicle detection approaches used have highly flexible implicit shape representations. They are based on training samples in both positive and negative sets and use updated classifiers to distinguish the targets. It has been found that the detection results attained by using HSV-GLCM texture features can be affected by blurring problems; the proposed detection algorithms can further segment the edges of the vehicles from the background. Using the point descriptor feature can solve the blurring problem, however, the large amount of information contained in point descriptors can lead to processing times that are too long for real-time applications. So the FAST-HoG approach combining the point feature and the shape feature is proposed. This new approach is able to speed up the process that attains the real-time performance. Finally, a detection approach using HoG with the FAST feature is also proposed. The HoG approach is widely used in object recognition, as it has a strong ability to represent the shape vector of the object. However, the original HoG feature is sensitive to the orientation of the target; this method improves the algorithm by inserting the direction vectors of the targets. For the tracking process, a novel tracking approach was proposed, an extension of the TLD algorithm, in order to track multiple targets. The extended approach upgrades the original system, which can only track a single target, which must be selected before the detection and tracking process. The greatest challenge to vehicle tracking is long-term tracking. The target object can change its appearance during the process and illumination and scale changes can also occur. The original TLD feature assumed that tracking can make errors during the tracking process, and the accumulation of these errors could cause tracking failure, so the original TLD proposed using a learning approach in between the tracking and the detection by adding a pair of inspectors (positive and negative) to constantly estimate errors. This thesis extends the TLD approach with a new detection method in order to achieve multiple-target tracking. A Forward and Backward Tracking approach has been proposed to eliminate tracking errors and other problems such as occlusion. The main purpose of the proposed tracking system is to learn the features of the targets during tracking and re-train the detection classifier for further processes. This thesis puts particular emphasis on vehicle detection and tracking in different extreme scenarios such as crowed highway vehicle detection, blurred images and changes in the appearance of the targets. Compared with currently existing detection and tracking approaches, the proposed approaches demonstrate a robust increase in accuracy in each scenario

Vehicle make and model recognition for intelligent transportation monitoring and surveillance.

Vehicle Make and Model Recognition (VMMR) has evolved into a significant subject of study due to its importance in numerous Intelligent Transportation Systems (ITS), such as autonomous navigation, traffic analysis, traffic surveillance and security systems. A highly accurate and real-time VMMR system significantly reduces the overhead cost of resources otherwise required. The VMMR problem is a multi-class classification task with a peculiar set of issues and challenges like multiplicity, inter- and intra-make ambiguity among various vehicles makes and models, which need to be solved in an efficient and reliable manner to achieve a highly robust VMMR system. In this dissertation, facing the growing importance of make and model recognition of vehicles, we present a VMMR system that provides very high accuracy rates and is robust to several challenges. We demonstrate that the VMMR problem can be addressed by locating discriminative parts where the most significant appearance variations occur in each category, and learning expressive appearance descriptors. Given these insights, we consider two data driven frameworks: a Multiple-Instance Learning-based (MIL) system using hand-crafted features and an extended application of deep neural networks using MIL. Our approach requires only image level class labels, and the discriminative parts of each target class are selected in a fully unsupervised manner without any use of part annotations or segmentation masks, which may be costly to obtain. This advantage makes our system more intelligent, scalable, and applicable to other fine-grained recognition tasks. We constructed a dataset with 291,752 images representing 9,170 different vehicles to validate and evaluate our approach. Experimental results demonstrate that the localization of parts and distinguishing their discriminative powers for categorization improve the performance of fine-grained categorization. Extensive experiments conducted using our approaches yield superior results for images that were occluded, under low illumination, partial camera views, or even non-frontal views, available in our real-world VMMR dataset. The approaches presented herewith provide a highly accurate VMMR system for rea-ltime applications in realistic environments.\\ We also validate our system with a significant application of VMMR to ITS that involves automated vehicular surveillance. We show that our application can provide law inforcement agencies with efficient tools to search for a specific vehicle type, make, or model, and to track the path of a given vehicle using the position of multiple cameras

Recognising high-level agent behaviour through observations in data scarce domains

This thesis presents a novel method for performing multi-agent behaviour recognition without requiring large training corpora. The reduced need for data means that robust probabilistic recognition can be performed within domains where annotated datasets are traditionally unavailable (e.g. surveillance, defence). Human behaviours are composed from sequences of underlying activities that can be used as salient features. We do not assume that the exact temporal ordering of such features is necessary, so can represent behaviours using an unordered “bag-of-features”. A weak temporal ordering is imposed during inference to match behaviours to observations and replaces the learnt model parameters used by competing methods. Our three-tier architecture comprises low-level video tracking, event analysis and high-level inference. High-level inference is performed using a new, cascading extension of the Rao-Blackwellised Particle Filter. Behaviours are recognised at multiple levels of abstraction and can contain a mixture of solo and multiagent behaviour. We validate our framework using the PETS 2006 video surveillance dataset and our own video sequences, in addition to a large corpus of simulated data. We achieve a mean recognition precision of 96.4% on the simulated data and 89.3% on the combined video data. Our “bag-of-features” framework is able to detect when behaviours terminate and accurately explains agent behaviour despite significant quantities of low-level classification errors in the input, and can even detect agents who change their behaviour

Factors Influencing Customer Satisfaction towards E-shopping in Malaysia

Online shopping or e-shopping has changed the world of business and quite a few people have decided to work with these features. What their primary concerns precisely and the responses from the globalisation are the competency of incorporation while doing their businesses. E-shopping has also increased substantially in Malaysia in recent years. The rapid increase in the e-commerce industry in Malaysia has created the demand to emphasize on how to increase customer satisfaction while operating in the e-retailing environment. It is very important that customers are satisfied with the website, or else, they would not return. Therefore, a crucial fact to look into is that companies must ensure that their customers are satisfied with their purchases that are really essential from the ecommerce’s point of view. With is in mind, this study aimed at investigating customer satisfaction towards e-shopping in Malaysia. A total of 400 questionnaires were distributed among students randomly selected from various public and private universities located within Klang valley area. Total 369 questionnaires were returned, out of which 341 questionnaires were found usable for further analysis. Finally, SEM was employed to test the hypotheses. This study found that customer satisfaction towards e-shopping in Malaysia is to a great extent influenced by ease of use, trust, design of the website, online security and e-service quality. Finally, recommendations and future study direction is provided. Keywords: E-shopping, Customer satisfaction, Trust, Online security, E-service quality, Malaysia