Person Re-Identification Using Hybrid Representation Reinforced by Metric Learning

Perwaiz, Nazia; Fraz, Muhammad Moazam; Shahzad, Muhammad

doi:10.1109/access.2018.2882254

Cited by 32 publications

(6 citation statements)

References 42 publications

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“…Human re-identification methods can be grouped into two categories, i.e., visual feature-based [2,3,29] and non-visual approaches [30,31]. Visual feature-based approaches rely on learning of features related to appearance and texture.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This is done by assigning a unique identification to individually detected persons the first time, followed by keeping track of them if identified at another location at a different time. A vast majority of the literature has addressed the person re-ID problem using vision sensors [2][3][4][5]. This is evident by the growing number of publications related to object re-ID appearing in top venues over recent years (see Figure 1).…”

Section: Introductionmentioning

confidence: 99%

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Person Re-Identification Using Deep Modeling of Temporally Correlated Inertial Motion Patterns

Gohar

Riaz

Shahzad

et al. 2020

Sensors

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Person re-identification (re-ID) is among the essential components that play an integral role in constituting an automated surveillance environment. Majorly, the problem is tackled using data acquired from vision sensors using appearance-based features, which are strongly dependent on visual cues such as color, texture, etc., consequently limiting the precise re-identification of an individual. To overcome such strong dependence on visual features, many researchers have tackled the re-identification problem using human gait, which is believed to be unique and provide a distinctive biometric signature that is particularly suitable for re-ID in uncontrolled environments. However, image-based gait analysis often fails to extract quality measurements of an individual’s motion patterns owing to problems related to variations in viewpoint, illumination (daylight), clothing, worn accessories, etc. To this end, in contrast to relying on image-based motion measurement, this paper demonstrates the potential to re-identify an individual using inertial measurements units (IMU) based on two common sensors, namely gyroscope and accelerometer. The experiment was carried out over data acquired using smartphones and wearable IMUs from a total of 86 randomly selected individuals including 49 males and 37 females between the ages of 17 and 72 years. The data signals were first segmented into single steps and strides, which were separately fed to train a sequential deep recurrent neural network to capture implicit arbitrary long-term temporal dependencies. The experimental setup was devised in a fashion to train the network on all the subjects using data related to half of the step and stride sequences only while the inference was performed on the remaining half for the purpose of re-identification. The obtained experimental results demonstrate the potential to reliably and accurately re-identify an individual based on one’s inertial sensor data.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Person Re-Identification Using Deep Modeling of Temporally Correlated Inertial Motion Patterns

Gohar

Riaz

Shahzad

et al. 2020

Sensors

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“…Usually, the local salient features are learnt from predefined local parts, patches, strips etc and are then integrated with global person representations to perform person re-id. Full-size DOI: 10.7717/peerjcs.447/fig- 1 Recently, with the success of attention based deep architectures in the field of computer vision (Zhang et al, 2018b;Woo et al, 2018;Mumtaz et al, 2017;mehdi Cherrat, Alaoui & Bouzahir, 2020;Zheng et al, 2019) the same are quickly opted for person re-identification domain as well (Li, Zhu & Gong, 2018;Liu et al, 2017;Perwaiz, Fraz & Shahzad, 2018;Mubariz et al, 2018). The attention based approaches extract local discriminative information by learning self-attention regions from the activation maps of convolutional layers instead of processing explicit predefined local parts of the image.…”

Section: Introductionmentioning

confidence: 99%

Stochastic attentions and context learning for person re-identification

Perwaiz

Fraz

Shahzad

2021

PeerJ Computer Science

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The discriminative parts of people’s appearance play a significant role in their re-identification across non overlapping camera views. However, just focusing on the discriminative or attention regions without catering the contextual information does not always help. It is more important to learn the attention with reference to their spatial locations in context of the whole image. Current person re-identification (re-id) approaches either use separate modules or classifiers to learn both of these; the attention and its context, resulting in highly expensive person re-id solutions. In this work, instead of handling attentions and the context separately, we employ a unified attention and context mapping (ACM) block within the convolutional layers of network, without any additional computational resources overhead. The ACM block captures the attention regions as well as the relevant contextual information in a stochastic manner and enriches the final person representations for robust person re-identification. We evaluate the proposed method on 04 public benchmarks of person re-identification i.e., Market1501, DukeMTMC-Reid, CUHK03 and MSMT17 and find that the ACM block consistently improves the performance of person re-identification over the baseline networks.

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“…Representation learning based on deep learning methods has been achieved remarkable performances in various visual recognition studies such as image classification [1][2][3], object recognition [4][5][6][7], face recognition [8][9][10][11], and person reidentification [12][13][14][15]. A key of these successes is an effective feature extraction via non-linear and cascaded kernel structures of deep neural networks.…”

Section: Introductionmentioning

confidence: 99%

Boosting Network Weight Separability via Feed-Backward Reconstruction

2020

IEEE Access

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This paper proposes a new evaluation metric and a boosting method for weight separability in neural network design. In contrast to general visual recognition methods designed to encourage both intraclass compactness and inter-class separability of latent features, we focus on estimating linear independence of column vectors in weight matrix and improving the separability of weight vectors. To this end, we propose an evaluation metric for weight separability based on semi-orthogonality of a matrix, Frobenius distance, and the feed-backward reconstruction loss, which explicitly encourages weight separability between the column vectors in the weight matrix. The experimental results on image classification and face recognition demonstrate that the weight separability boosting via minimization of feed-backward reconstruction loss can improve the visual recognition performance, hence universally boosting the performance on various visual recognition tasks.

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Person Re-Identification Using Hybrid Representation Reinforced by Metric Learning

Cited by 32 publications

References 42 publications

Person Re-Identification Using Deep Modeling of Temporally Correlated Inertial Motion Patterns

Person Re-Identification Using Deep Modeling of Temporally Correlated Inertial Motion Patterns

Stochastic attentions and context learning for person re-identification

Boosting Network Weight Separability via Feed-Backward Reconstruction

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