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70
M.: Person re-identification by symmetry-driven accumulation of local features
- In: IEEE Conf. Computer Vision and Pattern Recognition
, 2010
"... In this paper, we present an appearance-based method for person re-identification. It consists in the extraction of features that model three complementary aspects of the human appearance: the overall chromatic content, the spatial arrangement of colors into stable regions, and the presence of recur ..."
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Cited by 152 (6 self)
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In this paper, we present an appearance-based method for person re-identification. It consists in the extraction of features that model three complementary aspects of the human appearance: the overall chromatic content, the spatial arrangement of colors into stable regions, and the presence of recurrent local motifs with high entropy. All this information is derived from different body parts, and weighted opportunely by exploiting symmetry and asymmetry perceptual principles. In this way, robustness against very low resolution, occlusions and pose, viewpoint and illumination changes is achieved. The approach applies to situations where the number of candidates varies continuously, considering single images or bunch of frames for each individual. It has been tested on several public benchmark datasets (ViPER, iLIDS, ETHZ), gaining new state-of-the-art performances. 1.
Relaxed pairwise learned metric for person re-identification
- In ECCV
, 2012
"... Abstract. Matching persons across non-overlapping cameras is a rather challenging task. Thus, successful methods often build on complex feature representations or sophisticated learners. A recent trend to tackle this problem is to use metric learning to find a suitable space for matching samples fro ..."
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Cited by 55 (2 self)
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Abstract. Matching persons across non-overlapping cameras is a rather challenging task. Thus, successful methods often build on complex feature representations or sophisticated learners. A recent trend to tackle this problem is to use metric learning to find a suitable space for matching samples from different cameras. However, most of these approaches ignore the transition from one camera to the other. In this paper, we propose to learn a metric from pairs of samples from different cameras. In this way, even less sophisticated features describing color and texture information are sufficient for finally getting state-of-the-art classification results. Moreover, once the metric has been learned, only linear projections are necessary at search time, where a simple nearest neighbor classification is performed. The approach is demonstrated on three publicly available datasets of different complexity, where it can be seen that state-of-the-art results can be obtained at much lower computational costs. 1
Re-identification by relative distance comparison
- In PAMI
, 2013
"... Abstract—Matching people across nonoverlapping camera views at different locations and different times, known as person reidentification, is both a hard and important problem for associating behavior of people observed in a large distributed space over a prolonged period of time. Person reidentifica ..."
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Cited by 55 (8 self)
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Abstract—Matching people across nonoverlapping camera views at different locations and different times, known as person reidentification, is both a hard and important problem for associating behavior of people observed in a large distributed space over a prolonged period of time. Person reidentification is fundamentally challenging because of the large visual appearance changes caused by variations in view angle, lighting, background clutter, and occlusion. To address these challenges, most previous approaches aim to model and extract distinctive and reliable visual features. However, seeking an optimal and robust similarity measure that quantifies a wide range of features against realistic viewing conditions from a distance is still an open and unsolved problem for person reidentification. In this paper, we formulate person reidentification as a relative distance comparison (RDC) learning problem in order to learn the optimal similarity measure between a pair of person images. This approach avoids treating all features indiscriminately and does not assume the existence of some universally distinctive and reliable features. To that end, a novel relative distance comparison model is introduced. The model is formulated to maximize the likelihood of a pair of true matches having a relatively smaller distance than that of a wrong match pair in a soft discriminant manner. Moreover, in order to maintain the tractability of the model in large scale learning, we further develop an ensemble RDC model. Extensive experiments on three publicly available benchmarking datasets are carried out to demonstrate the clear superiority of the proposed RDC models over related popular person reidentification techniques. The results also show that the new RDC models are more robust against visual appearance changes and less susceptible to model overfitting compared to other related existing models. Index Terms—Person reidentification, feature quantification, feature selection, relative distance comparison Ç 1
H.: Person re-identification by descriptive and discriminative classification
- In: Proc. SCIA. (2011
"... Abstract. Person re-identification, i.e., recognizing a single person across spatially disjoint cameras, is an important task in visual surveillance. Existing approaches either try to find a suitable description of the appearance or learn a discriminative model. Since these different representationa ..."
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Cited by 44 (5 self)
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Abstract. Person re-identification, i.e., recognizing a single person across spatially disjoint cameras, is an important task in visual surveillance. Existing approaches either try to find a suitable description of the appearance or learn a discriminative model. Since these different representational strategies capture a large extent of complementary information we propose to combine both approaches. First, given a specific query, we rank all samples according to a feature-based similarity, where appearance is modeled by a set of region covariance descriptors. Next, a discriminative model is learned using boosting for feature selection, which provides a more specific classifier. The proposed approach is demonstrated on two datasets, where we show that the combination of a generic descriptive statistical model and a discriminatively learned feature-based model attains considerably better results than the individual models alone. In addition, we give a comparison to the state-of-the-art on a publicly available benchmark dataset. 1
Person reidentification: What features are important
- in ECCV Workshop on Person Re-identification
, 2012
"... Abstract. State-of-the-art person re-identification methods seek robust person matching through combining various feature types. Often, these features are implicitly assigned with a single vector of global weights, which are assumed to be universally good for all individuals, independent to their di ..."
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Cited by 38 (7 self)
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Abstract. State-of-the-art person re-identification methods seek robust person matching through combining various feature types. Often, these features are implicitly assigned with a single vector of global weights, which are assumed to be universally good for all individuals, independent to their different appearances. In this study, we show that certain features play more important role than others under different circumstances. Consequently, we propose a novel unsupervised approach for learning a bottom-up feature importance, so features extracted from different individuals are weighted adaptively driven by their unique and inherent appearance attributes. Extensive experiments on two public datasets demonstrate that attribute-sensitive feature importance facilitates more accurate person matching when it is fused together with global weights obtained using existing methods. 1
human re-identification by mean Riemannian covariance grid
- M Thonnat, in Advanced Video and Signal-Based Surveillance (AVSS), 2011 8th IEEE International Conference On. Multiple-shot
"... Human re-identification is defined as a requirement to determine whether a given individual has already appeared over a network of cameras. This problem is particularly hard by significant appearance changes across different camera views. In order to re-identify people a human signa-ture should hand ..."
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Cited by 35 (5 self)
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Human re-identification is defined as a requirement to determine whether a given individual has already appeared over a network of cameras. This problem is particularly hard by significant appearance changes across different camera views. In order to re-identify people a human signa-ture should handle difference in illumination, pose and cam-era parameters. We propose a new appearance model com-bining information from multiple images to obtain highly discriminative human signature, called Mean Riemannian Covariance Grid (MRCG). The method is evaluated and compared with the state of the art using benchmark video sequences from the ETHZ and the i-LIDS datasets. We demonstrate that the proposed approach outperforms state of the art methods. Finally, the results of our approach are shown on two other more pertinent datasets. 1.
X.: Locally aligned feature transforms across views
- In: IEEE International Conference on Computer Vision and Pattern Recognition
, 2013
"... In this paper, we propose a new approach for match-ing images observed in different camera views with com-plex cross-view transforms and apply it to person re-identification. It jointly partitions the image spaces of two camera views into different configurations according to the similarity of cross ..."
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Cited by 29 (2 self)
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In this paper, we propose a new approach for match-ing images observed in different camera views with com-plex cross-view transforms and apply it to person re-identification. It jointly partitions the image spaces of two camera views into different configurations according to the similarity of cross-view transforms. The visual fea-tures of an image pair from different views are first lo-cally aligned by being projected to a common feature space and then matched with softly assigned metrics which are locally optimized. The features optimal for recognizing identities are different from those for clustering cross-view transforms. They are jointly learned by utilizing sparsity-inducing norm and information theoretical regularization. This approach can be generalized to the settings where test images are from new camera views, not the same as those in the training set. Extensive experiments are conducted on public datasets and our own dataset. Comparisons with the state-of-the-art metric learning and person re-identification methods show the superior performance of our approach. 1.
Learning to Match Appearances by Correlations in a Covariance Metric Space
"... Abstract. This paper addresses the problem of appearance matching across disjoint camera views. Signi cant appearance changes, caused by variations in view angle, illumination and object pose, make the problem challenging. We propose to formulate the appearance matching problem as the task of learni ..."
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Cited by 22 (3 self)
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Abstract. This paper addresses the problem of appearance matching across disjoint camera views. Signi cant appearance changes, caused by variations in view angle, illumination and object pose, make the problem challenging. We propose to formulate the appearance matching problem as the task of learning a model that selects the most descriptive features for a speci c class of objects. Learning is performed in a covariance metric space using an entropy-driven criterion. Our main idea is that di erent regions of the object appearance ought to be matched using various strategies to obtain a distinctive representation. The proposed technique has been successfully applied to the person re-identi cation problem, in which a human appearance has to be matched across nonoverlapping cameras. We demonstrate that our approach improves state of the art performance in the context of pedestrian recognition.
Person Re-Identification by Efficient Impostor-based Metric Learning ∗
"... Recognizing persons over a system of disjunct cameras is a hard task for human operators and even harder for automated systems. In particular, realistic setups show difficulties such as different camera angles or different camera properties. Additionally, also the appearance of exactly the same pers ..."
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Cited by 19 (0 self)
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Recognizing persons over a system of disjunct cameras is a hard task for human operators and even harder for automated systems. In particular, realistic setups show difficulties such as different camera angles or different camera properties. Additionally, also the appearance of exactly the same person can change dramatically due to different views (e.g., frontal/back) of carried objects. In this paper, we mainly address the first problem by learning the transition from one camera to the other. This is realized by learning a Mahalanobis metric using pairs of labeled samples from different cameras. Building on the ideas of Large Margin Nearest Neighbor classification, we obtain a more efficient solution which additionally provides much better generalization properties. To demonstrate these benefits, we run experiments on three different publicly available datasets, showing state-of-the-art or even better results; however, on much lower computational efforts. This is in particular interesting since we use quite simple color and texture features, whereas other approaches build on rather complex image descriptions! 1.
Boosted human re-identification using riemannian manifolds
- Image and Vision Computing
, 2011
"... This paper presents an appearance-based model to address the human re-identification problem. Human re-identification is an important and still unsolved task in computer vision. In many systems there is a requirement to identify individuals or determine whether a given individual has already appeare ..."
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Cited by 19 (4 self)
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This paper presents an appearance-based model to address the human re-identification problem. Human re-identification is an important and still unsolved task in computer vision. In many systems there is a requirement to identify individuals or determine whether a given individual has already appeared somewhere in a network of cameras. The human appearance ob-tained in one camera is usually different from the ones obtained in another camera. In order to re-identify people a human signature should handle difference in illumination, pose and camera parameters. The paper focuses on a new appearance model based on Mean Riemannian Covariance (MRC) patches extracted from tracks of a particular individual. A new similarity measure using Riemannian manifold theory is also proposed to distinguish sets of patches belonging to a specific individual. We investigate the signifi-cance of the MRC patches based on their reliability extracted during tracking and their discriminative power obtained by a boosting scheme. The methods are evaluated and compared with the state of the art using benchmark video sequences from the ETHZ and the i-LIDS datasets. The re-identification per-formance is presented using the cumulative matching characteristic (CMC) curve. We demonstrate that the proposed approach outperforms state of the art methods. Finally, the results of our approach are shown on two other more pertinent datasets.
