Multi-shot Re-identification Research Articles

Joint graph regularized dictionary learning and sparse ranking for multi-modal multi-shot person re-identification

The promising achievement of sparse ranking in image-based recognition gives rise to a number of development on person re-identification (Re-ID) which aims to reconstruct the probe as a linear combination of few atoms/images from an over-complete dictionary/gallery. However, most of the existing sparse ranking based Re-ID methods lack considering the geometric relationships between probe, gallery, and cross-modal images of the same person in multi-shot Re-ID. In this paper, we propose a novel joint graph regularized dictionary learning and sparse ranking method for multi-modal multi-shot person Re-ID. First, we explore the probe-based geometrical structure by enforcing the smoothness between the codings/coefficients, which refers to the multi-shot images from the same person in probe. Second, we explore the gallery-based geometrical structure among gallery images, which encourages the multi-shot images from the same person in the gallery making similar contributions while reconstructing a certain probe image. Third, we explore the cross-modal geometrical structure by enforcing the smoothness between the cross-modal images and thus extend our model for the multi-modal case. Finally, we design an APG based optimization to solve the problem. Comprehensive experiments on benchmark datasets demonstrate the superior performance of the proposed model. The code is available at https://github.com/ttaalle/Lhc.

Learning Affine Hull Representations for Multi-Shot Person Re-Identification

We consider the person re-identification problem, assuming the availability of a sequence of images for each person, commonly referred to as video-based or multi-shot re-identification. We approach this problem from the perspective of learning discriminative distance metric functions. While existing distance metric learning methods typically employ the average feature vector as the data exemplar, this discards the inherent structure of the data. To overcome this issue, we describe the image sequence data using affine hulls. We show that directly computing the distance between the closest points on these affine hulls as in existing recognition algorithms is not sufficiently discriminative in the context of person re-identification. To this end, we incorporate affine hull data modeling into the traditional distance metric learning framework, learning discriminative feature representations directly using affine hulls. We perform extensive experiments on several publicly available data sets to show that the proposed approach improves the performance of existing metric learning algorithms irrespective of the feature space employed to perform metric learning. Furthermore, we advance the state of the art on iLIDS-VID, PRID, and SAIVT, with absolute rank-1 performance improvements of 6.0%, 11.4%, and 6.0% respectively.

Attributes driven tracklet-to-tracklet person re-identification using latent prototypes space mapping

Most of current person re-identification works identify a person by matching his/her probe image against a galley images set. One feasible way to improve the identification accuracy is the multi-shot re-identification, where the probe includes a small set of images rather than a single image. In this paper, we study the tracklet-to-tracklet identification, where both the probe and the target dataset are composed of small sets of sequential images, i.e., tracklets. To solve this problem and make our algorithm robust under multi-camera setting, we take full advantage of low-level features, attributes and inter-attribute correlations at the same time. Attributes are expected to offer semantic information complementary to low-level features. In order to discover the correlations among attributes, a novel discriminative model is proposed to exploit low-level features and map attributes to a discriminative latent prototypes space. An alternating optimization procedure is designed to perform the learning process. We also devise a number of voting schemes to total up matching scores from images to tracklets. Experiments on four public datasets show that our approach achieves a consistently better performance than existing person re-identification methods.

Person Re-Identification by Discriminative Selection in Video Ranking.

Current person re-identification (ReID) methods typically rely on single-frame imagery features, whilst ignoring space-time information from image sequences often available in the practical surveillance scenarios. Single-frame (single-shot) based visual appearance matching is inherently limited for person ReID in public spaces due to the challenging visual ambiguity and uncertainty arising from non-overlapping camera views where viewing condition changes can cause significant people appearance variations. In this work, we present a novel model to automatically select the most discriminative video fragments from noisy/incomplete image sequences of people from which reliable space-time and appearance features can be computed, whilst simultaneously learning a video ranking function for person ReID. Using the PRID 2011, iLIDS-VID, and HDA+ image sequence datasets, we extensively conducted comparative evaluations to demonstrate the advantages of the proposed model over contemporary gait recognition, holistic image sequence matching and state-of-the-art single-/multi-shot ReID methods.