Consistent Re-identification in a Camera Network

Das, Abir; Chakraborty, Anirban; Roy-Chowdhury, Amit K.

doi:10.1007/978-3-319-10605-2_22

Cited by 149 publications

(119 citation statements)

References 23 publications

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“…We show results in terms of recognition rate as cumulative matching characteristic (CMC) curves and normalized area-under-curve (nAUC) values, as is common practice in ReID literature [46][47][48][49][50][51][52][53]. The CMC curve is a plot of recognition performance versus re-identification ranking score, and represents the expectation of finding a correct match in the top k matches.…”

Section: Datasets and Settingsmentioning

confidence: 99%

“…We further quantified ReID performance using the graphs with P vs. nAUC. Following the literature [4,24,47,53], in all our experiments the gallery and the validation set are kept disjointed and we repeated each task 10 times by randomly picking the identities in validation and gallery. For each camera pair, we fixed the number of identities in the gallery to G = 50 for PRID 2011 and SAIVT-SoftBio, G = 25 for CAVIAR4REID and G = 316 for VIPeR.…”

Section: Datasets and Settingsmentioning

confidence: 99%

See 1 more Smart Citation

Exploiting Multiple Detections for Person Re-Identification

2018

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Re-identification systems aim at recognizing the same individuals in multiple cameras, and one of the most relevant problems is that the appearance of same individual varies across cameras due to illumination and viewpoint changes. This paper proposes the use of cumulative weighted brightness transfer functions (CWBTFs) to model these appearance variations. Different from recently proposed methods which only consider pairs of images to learn a brightness transfer function, we exploit such a multiple-frame-based learning approach that leverages consecutive detections of each individual to transfer the appearance. We first present a CWBTF framework for the task of transforming appearance from one camera to another. We then present a re-identification framework where we segment the pedestrian images into meaningful parts and extract features from such parts, as well as from the whole body. Jointly, both of these frameworks contribute to model the appearance variations more robustly. We tested our approach on standard multi-camera surveillance datasets, showing consistent and significant improvements over existing methods on three different datasets without any other additional cost. Our approach is general and can be applied to any appearance-based method.

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Section: Datasets and Settingsmentioning

confidence: 99%

Section: Datasets and Settingsmentioning

confidence: 99%

Exploiting Multiple Detections for Person Re-Identification

2018

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“…In contrast we compute similarities between entities and do not need to impose positive semidefinite conditions during training. Our method is also related to [43] where an integer optimization method was proposed to enforce network consistency in re-id during testing, i.e. maintaining consistency in re-id results across the network.…”

Section: Related Workmentioning

confidence: 99%

PRISM: Person Reidentification via Structured Matching

Zhang

Saligrama

2017

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-Person re-identification (re-id), an emerging problem in visual surveillance, deals with maintaining entities of individuals whilst they traverse various locations surveilled by a camera network. From a visual perspective re-id is challenging due to significant changes in visual appearance of individuals in cameras with different pose, illumination and calibration. Globally the challenge arises from the need to maintain structurally consistent matches among all the individual entities across different camera views. We propose PRISM, a structured matching method to jointly account for these challenges. We view the global problem as a weighted graph matching problem and estimate edge weights by learning to predict them based on the co-occurrences of visual patterns in the training examples. These co-occurrence based scores in turn account for appearance changes by inferring likely and unlikely visual co-occurrences appearing in training instances. We implement PRISM on single shot and multi-shot scenarios. PRISM uniformly outperforms state-of-the-art in terms of matching rate while being computationally efficient.

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“…Recently, Figueira et al [12] proposed a semi-supervised learning method to fuse multi-view features for Re-ID so that the features agree on the classification results. Das et al [5] considered the group membership prediction in Re-ID by maximizing the summation of pairwise similarity scores using binary integer programming during testing. Unlike [5], we formulate the group membership problem as a learning problem, rather than a post-processing step to improve the matching rate.…”

Section: Related Workmentioning

confidence: 99%

“…Das et al [5] considered the group membership prediction in Re-ID by maximizing the summation of pairwise similarity scores using binary integer programming during testing. Unlike [5], we formulate the group membership problem as a learning problem, rather than a post-processing step to improve the matching rate.…”

Section: Related Workmentioning

confidence: 99%

Group Membership Prediction

Zhang

Chen

Saligrama

2015

2015 IEEE International Conference on Computer Vision (ICCV)

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The group membership prediction (GMP) problem involves predicting whether or not a collection of instances share a certain semantic property. For instance, in kinship verification given a collection of images, the goal is to predict whether or not they share a familial relationship. In this context we propose a novel probability model and introduce latent view-specific and view-shared random variables to jointly account for the view-specific appearance and crossview similarities among data instances. Our model posits that data from each view is independent conditioned on the shared variables. This postulate leads to a parametric probability model that decomposes group membership likelihood into a tensor product of data-independent parameters and data-dependent factors. We propose learning the data-independent parameters in a discriminative way with bilinear classifiers, and test our prediction algorithm on challenging visual recognition tasks such as multi-camera person re-identification and kinship verification. On most benchmark datasets, our method can significantly outperform the current state-of-the-art.

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Consistent Re-identification in a Camera Network

Cited by 149 publications

References 23 publications

Exploiting Multiple Detections for Person Re-Identification

Exploiting Multiple Detections for Person Re-Identification

PRISM: Person Reidentification via Structured Matching

Group Membership Prediction

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