Maritime vessel re-identification: novel VR-VCA dataset and a multi-branch architecture MVR-net

Maritime vessel re-identification (re-ID) is a computer vision task of vessel identity matching across disjoint camera views. Prominent applications of vessel re-ID exist in the fields of surveillance and maritime traffic flow analysis. However, the field suffers from the absence of a large-scale dataset that enables training of deep learning models. In this study, we present a new dataset that includes 4614 images of 729 vessels along with 5-bin orientation and 8-class vessel-type annotations to promote further research. A second contribution of this study is the baseline re-ID analysis of our new dataset. Performances of 10 recent deep learning architectures are quantitatively compared to reveal the best practices. Lastly, we propose a novel multi-branch deep learning architecture, Maritime Vessel Re-ID network (MVR-net), to address the challenging problem of vessel re-ID. Evaluation of our approach on the new dataset yields 74.5% mAP and 77.9% Rank-1 score, providing a performance increase of 5.7% mAP and 5.0% Rank-1 over the best-performing baseline. MVR-net also outperforms the PRN (a pioneering vehicle re-ID network), by 2.9% and 4.3% higher mAP and Rank-1, respectively.

Person Re-Identification across Data Distributions Based on General Purpose DNN Object Detector

Solving the person re-identification problem involves making associations between the same person’s appearances across disjoint camera views. Further, those associations have to be made on multiple surveillance cameras in order to obtain a more efficient and powerful re-identification system. The re-identification problem becomes particularly challenging in very crowded areas. This mainly happens for two reasons. First, the visibility is reduced and occlusions of people can occur. Further, due to congestion, as the number of possible matches increases, the re-identification is becoming challenging to achieve. Additional challenges consist of variations of lightning, poses, or viewpoints, and the existence of noise and blurring effects. In this paper, we aim to generalize person re-identification by implementing a first attempt of a general system, which is robust in terms of distribution variations. Our method is based on the YOLO (You Only Look Once) model, which represents a general object detection system. The novelty of the proposed re-identification method consists of using a simple detection model, with minimal additional costs, but with results that are comparable with those of the other existing dedicated methods.

A New Date-Balanced Method Based on Adaptive Asymmetric and Diversity Regularization in Person Re-Identification

Person re-identification (person re-ID) is a challenging task which aims at spotting same persons among disjoint camera views. It has certainly generated a lot of attention in the field of computer vision, but it remains a challenging task due to the complexity of person appearances from different camera views. To solve this challenging problem, many excellent methods have been proposed, especially metric learning-based algorithms. However, most of them suffer from the problem of data imbalance. To solve this problem, in the paper we proposed a new data-balanced method and named it Enhanced Metric Learning (EML) based on adaptive asymmetric and diversity regularization for person re-ID. Metric learning is important for person re-ID because it can eliminate the negative effects caused by camera differences to a certain extent. But most metric learning approaches often neglect the problem of data imbalance caused by too many negative samples but few positive samples. And they often treat all negative samples the same as positive ones, which can lead to the loss of important information. Our approach pays different attention to the positive samples and negative ones. Firstly, we classified negative samples into three groups adaptively, and then paid different attention to them using adaptive asymmetric strategy. By treating samples differently, the proposed method can better exploit the discriminative information between positive and negative samples. Furthermore, we also proposed to impose a diversity regularizer to avoid over-fitting when the training sets are small or medium-sized. Finally, we designed a series of experiments on four challenging databases (VIPeR, PRID450S, CUHK01 and GRID), to compare with some excellent metric learning methods. Experimental results show that the rank-1 matching rate of the proposed method has outperformed the state-of-the-art by 3.64%, 4.2%, 3.13% and 2.83% on the four databases, respectively.

Co-Metric Learning for Person Re-Identification

Person re-identification, aiming to identify the same pedestrian images across disjoint camera views, is a key technique of intelligent video surveillance. Although existing methods have developed both theories and experimental results, most of effective ones pertain to fully supervised training styles, which suffer the small sample size (SSS) problem a lot, especially in label-insufficient practical applications. To bridge SSS problem and learning model with small labels, a novel semisupervised co-metric learning framework is proposed to learn a discriminative Mahalanobis-like distance matrix for label-insufficient person re-identification. Different from typical co-training task that contains multiview data originally, single-view person images are firstly decomposed into pseudo two views, and then metric learning models are produced and jointly updated based on both pseudo-labels and references iteratively. Experiments carried out on three representative person re-identification datasets show that the proposed method performs better than state of the art and possesses low label sensitivity.

Adversarial Attribute-Image Person Re-identification

While attributes have been widely used for person re-identification (Re-ID) which aims at matching the same person images across disjoint camera views, they are used either as extra features or for performing multi-task learning to assist the image-image matching task. However, how to find a set of person images according to a given attribute description, which is very practical in many surveillance applications, remains a rarely investigated cross-modality matching problem in person Re-ID. In this work, we present this challenge and leverage adversarial learning to formulate the attribute-image cross-modality person Re-ID model. By imposing a semantic consistency constraint across modalities as a regularization, the adversarial learning enables to generate image-analogous concepts of query attributes for matching the corresponding images at both global level and semantic ID level. We conducted extensive experiments on three attribute datasets and demonstrated that the regularized adversarial modelling is so far the most effective method for the attribute-image cross-modality person Re-ID problem.

Appearance Model Based Moving Object Matching across Disjoint Camera Views

A new object tracking scheme for multi-camera surveillance with non-overlapping views is proposed in this paper. Brightness transfer function (BTF) is used to establish relative appearance correspondence between different views. Mixtures of probabilistic principal component analysis (MPPCA) is incooperated to learn the subspace of brightness transfer function with the concern to deal with multiple different brightness areas in a scene. The incremental major color spectrum histogram (IMCSH) is used as similarity measure for reliable matching. Experimental results with real world videos show the effectiveness of the proposed algorithm.