Temporal Model Adaptation for Person Re-Identification

Person re-identification is an open and challenging problem in computer vision. Majority of the efforts have been spent either to design the best feature representation or to learn the optimal matching metric. Most approaches have neglected the problem of adapting the selected features or the learned model over time. To address such a problem, we propose a temporal model adaptation scheme with human in the loop. We first introduce a similarity-dissimilarity learning method which can be trained in an incremental fashion by means of a stochastic alternating directions methods of multipliers optimization procedure. Then, to achieve temporal adaptation with limited human effort, we exploit a graph-based approach to present the user only the most informative probe-gallery matches that should be used to update the model. Results on three datasets have shown that our approach performs on par or even better than state-of-the-art approaches while reducing the manual pairwise labeling effort by about 80%

Pose-Normalized Image Generation for Person Re-identification

Person Re-identification (re-id) faces two major challenges: the lack of cross-view paired training data and learning discriminative identity-sensitive and view-invariant features in the presence of large pose variations. In this work, we address both problems by proposing a novel deep person image generation model for synthesizing realistic person images conditional on the pose. The model is based on a generative adversarial network (GAN) designed specifically for pose normalization in re-id, thus termed pose-normalization GAN (PN-GAN). With the synthesized images, we can learn a new type of deep re-id feature free of the influence of pose variations. We show that this feature is strong on its own and complementary to features learned with the original images. Importantly, under the transfer learning setting, we show that our model generalizes well to any new re-id dataset without the need for collecting any training data for model fine-tuning. The model thus has the potential to make re-id model truly scalable.Comment: 10 pages, 5 figure

Vehicle Re-identification in Context

© 2019, Springer Nature Switzerland AG. Existing vehicle re-identification (re-id) evaluation benchmarks consider strongly artificial test scenarios by assuming the availability of high quality images and fine-grained appearance at an almost constant image scale, reminiscent to images required for Automatic Number Plate Recognition, e.g. VeRi-776. Such assumptions are often invalid in realistic vehicle re-id scenarios where arbitrarily changing image resolutions (scales) are the norm. This makes the existing vehicle re-id benchmarks limited for testing the true performance of a re-id method. In this work, we introduce a more realistic and challenging vehicle re-id benchmark, called Vehicle Re-Identification in Context (VRIC). In contrast to existing vehicle re-id datasets, VRIC is uniquely characterised by vehicle images subject to more realistic and unconstrained variations in resolution (scale), motion blur, illumination, occlusion, and viewpoint. It contains 60,430 images of 5,622 vehicle identities captured by 60 different cameras at heterogeneous road traffic scenes in both day-time and night-time. Given the nature of this new benchmark, we further investigate a multi-scale matching approach to vehicle re-id by learning more discriminative feature representations from multi-resolution images. Extensive evaluations show that the proposed multi-scale method outperforms the state-of-the-art vehicle re-id methods on three benchmark datasets: VehicleID, VeRi-776, and VRIC (Available at http://qmul-vric.github.io )

Accelerated low-rank sparse metric learning for person re-identification

Person re-identification is an open and challenging problem in computer vision. A surge of effort has been spent design the best feature representation, and to learn either the transformation of such features across cameras or an optimal matching metric. Metric learning solutions which are currently in vogue in the field generally require a dimensionality reduction pre-processing stage to handle the high-dimensionality of the adopted feature representation. Such an approach is suboptimal and a better solution can be achieved by combining such a step in the metric learning process. Towards this objective, a low-rank matrix which projects the high-dimensional vectors to a low-dimensional manifold with a discriminative Euclidean distance is introduced. The goal is achieved with a stochastic accelerated proximal gradient method. Experiments on two public benchmark datasets show that better performances than state-of-the-art methods are achieved

Re-Identify people in wide area camera network

Tracking individuals within a wide area camera network is a tough problem. Obtaining information across uncovered areas is an open issue that person re-identification methods deal with. A novel appearance-based method for person re-identification is proposed. The approach computes a novel discriminative signature by exploiting multiple local features. A novel signature distance measure is given by exploiting a body part division approach. The method has been compared to state-of-the-art methods using a re-identification benchmark dataset. A new dataset acquired from non-overlapping cameras has been built to validate the method against a real wide area camera network scenario. The method has proven to be robust against low resolution images, viewpoint and illumination changes, occlusions and pose variations. Results show that the proposed approach outperforms state-of-the-art methods used for compariso

Oriented Splits Network to Distill Background for Vehicle Re-Identification

Vehicle re-identification (re-id) is a challenging task due to the presence of high intra-class and low inter-class variations in the visual data acquired from monitoring camera networks. Unique and discriminative feature representations are needed to overcome the existence of several variations including color, illumination, orientation, background and occlusion. The orientations of the vehicles in the images make the learned models unable to learn multiple parts of the vehicle and relationship between them. The combination of global and partial features is one of the solutions to improve the discriminative learning of deep learning models. Leveraging on such solutions, we propose an Oriented Splits Network (OSN) for an end to end learning of multiple features along with global features to form a strong descriptor for vehicle re-identification. To capture the orientation variability of the vehicles, the proposed network introduces a partition of the images into several oriented stripes to obtain local descriptors for each part/region. Such a scheme is therefore exploited by a camera based feature distillation (CBD) training strategy to remove the background features. These are filtered out from oriented vehicles representations which yield to a much stronger unique representation of the vehicles. We perform experiments on two benchmark vehicle re-id datasets to verify the performance of the proposed approach which show that the proposed solution achieves better result with respect to the state of the art with margin

An ensemble feature method for food classification

In the last years, several works on automatic image-based food recognition have been proposed, often based on texture feature extraction and classification. However, there is still a lack of proper comparisons to evaluate which approaches are better suited for this specific task. In this work, we adopt a Random Forest classifier to measure the performances of different texture filter banks and feature encoding techniques on three different food image datasets. Comparative results are given to show the performance of each considered approach, as well as to compare the proposed Random Forest classifiers with other feature-based state-of-the-art solutions

Weakly-Supervised Domain Adaptation of Deep Regression Trackers via Reinforced Knowledge Distillation

Deep regression trackers are among the fastest tracking algorithms available, and therefore suitable for real-time robotic applications. However, their accuracy is inadequate in many domains due to distribution shift and overfitting. In this paper we overcome such limitations by presenting the first methodology for domain adaption of such a class of trackers. To reduce the labeling effort we propose a weakly-supervised adaptation strategy, in which reinforcement learning is used to express weak supervision as a scalar application-dependent and temporally-delayed feedback. At the same time, knowledge distillation is employed to guarantee learning stability and to compress and transfer knowledge from more powerful but slower trackers. Extensive experiments on five different domains demonstrate the relevance of our methodology. Real-time speed is achieved on embedded devices and on machines without GPUs, while accuracy reaches significant results