Learning Deep Context-aware Features over Body and Latent Parts for Person Re-identification

Person Re-identification (ReID) is to identify the same person across different cameras. It is a challenging task due to the large variations in person pose, occlusion, background clutter, etc How to extract powerful features is a fundamental problem in ReID and is still an open problem today. In this paper, we design a Multi-Scale Context-Aware Network (MSCAN) to learn powerful features over full body and body parts, which can well capture the local context knowledge by stacking multi-scale convolutions in each layer. Moreover, instead of using predefined rigid parts, we propose to learn and localize deformable pedestrian parts using Spatial Transformer Networks (STN) with novel spatial constraints. The learned body parts can release some difficulties, eg pose variations and background clutters, in part-based representation. Finally, we integrate the representation learning processes of full body and body parts into a unified framework for person ReID through multi-class person identification tasks. Extensive evaluations on current challenging large-scale person ReID datasets, including the image-based Market1501, CUHK03 and sequence-based MARS datasets, show that the proposed method achieves the state-of-the-art results.Comment: Accepted by CVPR 201

Learning Discriminative Features for Person Re-Identification

For fulfilling the requirements of public safety in modern cities, more and more large-scale surveillance camera systems are deployed, resulting in an enormous amount of visual data. Automatically processing and interpreting these data promote the development and application of visual data analytic technologies. As one of the important research topics in surveillance systems, person re-identification (re-id) aims at retrieving the target person across non-overlapping camera-views that are implemented in a number of distributed space-time locations. It is a fundamental problem for many practical surveillance applications, eg, person search, cross-camera tracking, multi-camera human behavior analysis and prediction, and it received considerable attentions nowadays from both academic and industrial domains. Learning discriminative feature representation is an essential task in person re-id. Although many methodologies have been proposed, discriminative re-id feature extraction is still a challenging problem due to: (1) Intra- and inter-personal variations. The intrinsic properties of the camera deployment in surveillance system lead to various changes in person poses, view-points, illumination conditions etc. This may result in the large intra-personal variations and/or small inter-personal variations, thus incurring problems in matching person images. (2) Domain variations. The domain variations between different datasets give rise to the problem of generalization capability of re-id model. Directly applying a re-id model trained on one dataset to another one usually causes a large performance degradation. (3) Difficulties in data creation and annotation. Existing person re-id methods, especially deep re-id methods, rely mostly on a large set of inter-camera identity labelled training data, requiring a tedious data collection and annotation process. This leads to poor scalability in practical person re-id applications. Corresponding to the challenges in learning discriminative re-id features, this thesis contributes to the re-id domain by proposing three related methodologies and one new re-id setting: (1) Gaussian mixture importance estimation. Handcrafted features are usually not discriminative enough for person re-id because of noisy information, such as background clutters. To precisely evaluate the similarities between person images, the main task of distance metric learning is to filter out the noisy information. Keep It Simple and Straightforward MEtric (KISSME) is an effective method in person re-id. However, it is sensitive to the feature dimensionality and cannot capture the multi-modes in dataset. To this end, a Gaussian Mixture Importance Estimation re-id approach is proposed, which exploits the Gaussian Mixture Models for estimating the observed commonalities of similar and dissimilar person pairs in the feature space. (2) Unsupervised domain-adaptive person re-id based on pedestrian attributes. In person re-id, person identities are usually not overlapped among different domains (or datasets) and this raises the difficulties in generalizing re-id models. Different from person identity, pedestrian attributes, eg., hair length, clothes type and color, are consistent across different domains (or datasets). However, most of re-id datasets lack attribute annotations. On the other hand, in the field of pedestrian attribute recognition, there is a number of datasets labeled with attributes. Exploiting such data for re-id purpose can alleviate the shortage of attribute annotations in re-id domain and improve the generalization capability of re-id model. To this end, an unsupervised domain-adaptive re-id feature learning framework is proposed to make full use of attribute annotations. Specifically, an existing unsupervised domain adaptation method has been extended to transfer attribute-based features from attribute recognition domain to the re-id domain. With the proposed re-id feature learning framework, the domain invariant feature representations can be effectively extracted. (3) Intra-camera supervised person re-id. Annotating the large-scale re-id datasets requires a tedious data collection and annotation process and therefore leads to poor scalability in practical person re-id applications. To overcome this fundamental limitation, a new person re-id setting is considered without inter-camera identity association but only with identity labels independently annotated within each camera-view. This eliminates the most time-consuming and tedious inter-camera identity association annotating process and thus significantly reduces the amount of human efforts required during annotation. It hence gives rise to a more scalable and more feasible learning scenario, which is named as Intra-Camera Supervised (ICS) person re-id. Under this ICS setting, a new re-id method, i.e., Multi-task Mulit-label (MATE) learning method, is formulated. Given no inter-camera association, MATE is specially designed for self-discovering the inter-camera identity correspondence. This is achieved by inter-camera multi-label learning under a joint multi-task inference framework. In addition, MATE can also efficiently learn the discriminative re-id feature representations using the available identity labels within each camera-view

Re-identification and semantic retrieval of pedestrians in video surveillance scenarios

Person re-identification consists of recognizing individuals across different sensors of a camera network. Whereas clothing appearance cues are widely used, other modalities could be exploited as additional information sources, like anthropometric measures and gait. In this work we investigate whether the re-identification accuracy of clothing appearance descriptors can be improved by fusing them with anthropometric measures extracted from depth data, using RGB-Dsensors, in unconstrained settings. We also propose a dissimilaritybased framework for building and fusing multi-modal descriptors of pedestrian images for re-identification tasks, as an alternative to the widely used score-level fusion. The experimental evaluation is carried out on two data sets including RGB-D data, one of which is a novel, publicly available data set that we acquired using Kinect sensors. In this dissertation we also consider a related task, named semantic retrieval of pedestrians in video surveillance scenarios, which consists of searching images of individuals using a textual description of clothing appearance as a query, given by a Boolean combination of predefined attributes. This can be useful in applications like forensic video analysis, where the query can be obtained froma eyewitness report. We propose a general method for implementing semantic retrieval as an extension of a given re-identification system that uses any multiple part-multiple component appearance descriptor. Additionally, we investigate on deep learning techniques to improve both the accuracy of attribute detectors and generalization capabilities. Finally, we experimentally evaluate our methods on several benchmark datasets originally built for re-identification task

Tracklet and Signature Representation for Multi-shot Person Re-Identification.

International audienceVideo surveillance has become more and more important in many domains for their security andsafety. Person Re-Identification (Re-ID) is one of the most interesting subjects in this area. The Re-ID systemis divided into two main stages: i) extracting feature representations to construct a person’s appearance sig-nature and ii) establishing the correspondence/matching by learning similarity metrics or ranking functions.However, appearance based person Re-ID is a challenging task due to similarity of human’s appearance andvisual ambiguities across different cameras. This paper provides a representation of the appearance descriptors,called signatures, for multi-shot Re-ID. First, we will present the tracklets, i.e trajectories of persons. Then,we compute the signature and represent it based on the approach of Part Appearance Mixture (PAM). Anevaluation of the quality of this signature representation is also described in order to essentially solve the problemsof high variance in a person’s appearance, occlusions, illumination changes and person’s orientation/pose. Todeal with variance in a person’s appearance, we represent it as a set of multi-modal feature distributions modeledby Gaussian Mixture Model (GMM). Experiments and results on two public datasets and on our own datasetshow good performance