DyGLIP: A Dynamic Graph Model with Link Prediction for Accurate Multi-Camera Multiple Object Tracking

Multi-Camera Multiple Object Tracking (MC-MOT) is a significant computer vision problem due to its emerging applicability in several real-world applications. Despite a large number of existing works, solving the data association problem in any MC-MOT pipeline is arguably one of the most challenging tasks. Developing a robust MC-MOT system, however, is still highly challenging due to many practical issues such as inconsistent lighting conditions, varying object movement patterns, or the trajectory occlusions of the objects between the cameras. To address these problems, this work, therefore, proposes a new Dynamic Graph Model with Link Prediction (DyGLIP) approach to solve the data association task. Compared to existing methods, our new model offers several advantages, including better feature representations and the ability to recover from lost tracks during camera transitions. Moreover, our model works gracefully regardless of the overlapping ratios between the cameras. Experimental results show that we outperform existing MC-MOT algorithms by a large margin on several practical datasets. Notably, our model works favorably on online settings but can be extended to an incremental approach for large-scale datasets.Comment: accepted at CVPR 202

Novel Aggregated Solutions for Robust Visual Tracking in Traffic Scenarios

This work proposes novel approaches for object tracking in challenging scenarios like severe occlusion, deteriorated vision and long range multi-object reidentification. All these solutions are only based on image sequence captured by a monocular camera and do not require additional sensors. Experiments on standard benchmarks demonstrate an improved state-of-the-art performance of these approaches. Since all the presented approaches are smartly designed, they can run at a real-time speed

Computational Aesthetics for Fashion

The online fashion industry is growing fast and with it, the need for advanced systems able to automatically solve different tasks in an accurate way. With the rapid advance of digital technologies, Deep Learning has played an important role in Computational Aesthetics, an interdisciplinary area that tries to bridge fine art, design, and computer science. Specifically, Computational Aesthetics aims to automatize human aesthetic judgments with computational methods. In this thesis, we focus on three applications of computer vision in fashion, and we discuss how Computational Aesthetics helps solve them accurately

Person re-Identification over distributed spaces and time

PhDReplicating the human visual system and cognitive abilities that the brain uses to process the information it receives is an area of substantial scientific interest. With the prevalence of video surveillance cameras a portion of this scientific drive has been into providing useful automated counterparts to human operators. A prominent task in visual surveillance is that of matching people between disjoint camera views, or re-identification. This allows operators to locate people of interest, to track people across cameras and can be used as a precursory step to multi-camera activity analysis. However, due to the contrasting conditions between camera views and their effects on the appearance of people re-identification is a non-trivial task. This thesis proposes solutions for reducing the visual ambiguity in observations of people between camera views This thesis first looks at a method for mitigating the effects on the appearance of people under differing lighting conditions between camera views. This thesis builds on work modelling inter-camera illumination based on known pairs of images. A Cumulative Brightness Transfer Function (CBTF) is proposed to estimate the mapping of colour brightness values based on limited training samples. Unlike previous methods that use a mean-based representation for a set of training samples, the cumulative nature of the CBTF retains colour information from underrepresented samples in the training set. Additionally, the bi-directionality of the mapping function is explored to try and maximise re-identification accuracy by ensuring samples are accurately mapped between cameras. Secondly, an extension is proposed to the CBTF framework that addresses the issue of changing lighting conditions within a single camera. As the CBTF requires manually labelled training samples it is limited to static lighting conditions and is less effective if the lighting changes. This Adaptive CBTF (A-CBTF) differs from previous approaches that either do not consider lighting change over time, or rely on camera transition time information to update. By utilising contextual information drawn from the background in each camera view, an estimation of the lighting change within a single camera can be made. This background lighting model allows the mapping of colour information back to the original training conditions and thus remove the need for 3 retraining. Thirdly, a novel reformulation of re-identification as a ranking problem is proposed. Previous methods use a score based on a direct distance measure of set features to form a correct/incorrect match result. Rather than offering an operator a single outcome, the ranking paradigm is to give the operator a ranked list of possible matches and allow them to make the final decision. By utilising a Support Vector Machine (SVM) ranking method, a weighting on the appearance features can be learned that capitalises on the fact that not all image features are equally important to re-identification. Additionally, an Ensemble-RankSVM is proposed to address scalability issues by separating the training samples into smaller subsets and boosting the trained models. Finally, the thesis looks at a practical application of the ranking paradigm in a real world application. The system encompasses both the re-identification stage and the precursory extraction and tracking stages to form an aid for CCTV operators. Segmentation and detection are combined to extract relevant information from the video, while several combinations of matching techniques are combined with temporal priors to form a more comprehensive overall matching criteria. The effectiveness of the proposed approaches is tested on datasets obtained from a variety of challenging environments including offices, apartment buildings, airports and outdoor public spaces

Re-identifying people in the crowd

Developing an automated surveillance system is of great interest for various reasons including forensic and security applications. In the case of a network of surveillance cameras with non-overlapping fields of view, person detection and tracking alone are insufficient to track a subject of interest across the network. In this case, instances of a person captured in one camera view need to be retrieved among a gallery of different people, in other camera views. This vision problem is commonly known as person re-identification (re-id). Cross-view instances of pedestrians exhibit varied levels of illumination, viewpoint, and pose variations which makes the problem very challenging. Despite recent progress towards improving accuracy, existing systems suffer from low applicability to real-world scenarios. This is mainly caused by the need for large amounts of annotated data from pairwise camera views to be available for training. Given the difficulty of obtaining such data and annotating it, this thesis aims to bring the person re-id problem a step closer to real-world deployment. In the first contribution, the single-shot protocol, where each individual is represented by a pair of images that need to be matched, is considered. Following the extensive annotation of four datasets for six attributes, an evaluation of the most widely used feature extraction schemes is conducted. The results reveal two high-performing descriptors among those evaluated, and show illumination variation to have the most impact on re-id accuracy. Motivated by the wide availability of videos from surveillance cameras and the additional visual and temporal information they provide, video-based person re-id is then investigated, and a su-pervised system is developed. This is achieved by improving and extending the best performing image-based person descriptor into three dimensions and combining it with distance metric learn-ing. The system obtained achieves state-of-the-art results on two widely used datasets. Given the cost and difficulty of obtaining labelled data from pairwise cameras in a network to train the model, an unsupervised video-based person re-id method is also developed. It is based on a set-based distance measure that leverages rank vectors to estimate the similarity scores between person tracklets. The proposed system outperforms other unsupervised methods by a large margin on two datasets while competing with deep learning methods on another large-scale dataset

Applications of a Graph Theoretic Based Clustering Framework in Computer Vision and Pattern Recognition

Recently, several clustering algorithms have been used to solve variety of problems from different discipline. This dissertation aims to address different challenging tasks in computer vision and pattern recognition by casting the problems as a clustering problem. We proposed novel approaches to solve multi-target tracking, visual geo-localization and outlier detection problems using a unified underlining clustering framework, i.e., dominant set clustering and its extensions, and presented a superior result over several state-of-the-art approaches.Comment: doctoral dissertatio

Learning Person Re-identification Models from Videos with Weak Supervision

Most person re-identification methods, being supervised techniques, suffer from the burden of massive annotation requirement. Unsupervised methods overcome this need for labeled data, but perform poorly compared to the supervised alternatives. In order to cope with this issue, we introduce the problem of learning person re-identification models from videos with weak supervision. The weak nature of the supervision arises from the requirement of video-level labels, i.e. person identities who appear in the video, in contrast to the more precise framelevel annotations. Towards this goal, we propose a multiple instance attention learning framework for person re-identification using such video-level labels. Specifically, we first cast the video person re-identification task into a multiple instance learning setting, in which person images in a video are collected into a bag. The relations between videos with similar labels can be utilized to identify persons, on top of that, we introduce a co-person attention mechanism which mines the similarity correlations between videos with person identities in common. The attention weights are obtained based on all person images instead of person tracklets in a video, making our learned model less affected by noisy annotations. Extensive experiments demonstrate the superiority of the proposed method over the related methods on two weakly labeled person re-identification datasets