Human Reidentification with Transferred Metric Learning

Wei, Li; Zhao, Rui; Wang, Xiaogang

doi:10.1007/978-3-642-37331-2_3

Cited by 423 publications

(329 citation statements)

References 28 publications

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“…We utilize the pedestrian images from the public available dataset CUHK Person Re-identification Dataset [4] to evaluate our approach. The pedestrian images in this dataset are collected from the campus where the bag appearance and location change greatly, as seen in Fig.8.…”

Section: Methodsmentioning

confidence: 99%

“…In visual surveillance, people are interested in automatically searching persons from a huge amount of video data [1][2][3][4][5][6][7][8][9][10][11]. Because bag is a very common target appeared in surveillance video from public areas such as streets, subways, tourist attractions, airports and supermarkets, mining bag information is conducive to criminals monitoring, lost person search, video index and criminal investigation, and so on.…”

Section: Introductionmentioning

confidence: 99%

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A Two-Stage Approach for Bag Detection in Pedestrian Images

Lao

2015

Computer Vision -- ACCV 2014

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Abstract. Bag detection in pedestrian images is a very practical visual surveillance problem. It is challenging because bag appearance may vary greatly. In this paper, we propose a novel two-stage approach for bag detection in pedestrian images. Firstly, we utilize two stripe vocabulary forests to check whether a pedestrian is with a bag. Secondly, we locate the bag location by ranking the generated bottom-up region proposals. The ranker is learned with a convolutional neural network (CNN). Experiments are performed on a subset of CUHK person re-identification dataset that show the effectiveness of our approach for bag detection in pedestrian images. Although developed for a specific problem, our approach could be applied to detect other carrying objects in pedestrian images.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Two-Stage Approach for Bag Detection in Pedestrian Images

Lao

2015

Computer Vision -- ACCV 2014

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“…CUHK01 [15] consists of front view and back view images of 972 people which are used as gallery and probe images in the experiment. The images in CUHK01 are resized to 160 × 60 for standardization.…”

Section: Experimental Settingsmentioning

confidence: 99%

“…In this work, we extensively consider four state-of-the-art person re-identification methods including SDALF [6], QAF [33], Mid-level filter [32], and SDC knn [30]. These methods were evaluated on three different benchmark datasets: VIPeR [10], ETHZ [5,26], and CUHK01 [15]. By doing this, we expect to provide a comprehensive evaluation of the proposed approach.…”

Section: Introductionmentioning

confidence: 99%

Person re-identification with mutual re-ranking

Nguyen

Ngo

et al. 2017

Vietnam J Comput Sci

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Person re-identification is the problem of identifying people moving across cameras. Traditional approaches deal with this problem by pair-wise matching images recorded from two different cameras. A person in the second camera is identified by comparing his image with images in the first camera, independently of other persons in the second camera. In reality, there are many situations in which multiple persons appear concurrently in the second camera. In this paper, we propose a method for postprocessing re-identification results. The idea is to utilize information of co-occurrence persons for comparing and re-arranging given ranked lists. Experiments conducted on different datasets with several state-of-the-art methods have shown the effectiveness of our post-processing method in improving re-identification accuracy.

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“…We train these models on a variety of large benchmark datasets including VIPER [28] (632 distinct persons in [128x48] crops), PRID [13] (200 distinct persons), GRID [22] (250 persons) and CUHK [21] (971 persons). We resample all detections to match VIPeR's dimensions.…”

Section: Classifier Training Representation and Datasetsmentioning

confidence: 99%

Investigating Open-World Person Re-identification Using a Drone

Layne

Hospedales

Gong

2015

Lecture Notes in Computer Science

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Abstract. Person re-identification is now one of the most topical and intensively studied problems in computer vision due to its challenging nature and its critical role in underpinning many multi-camera surveillance tasks. A fundamental assumption in almost all existing re-identification research is that cameras are in fixed emplacements, allowing the explicit modelling of camera and inter-camera properties in order to improve re-identification. In this paper, we present an introductory study pushing re-identification in a different direction: re-identification on a mobile platform, such as a drone. We formalise some variants of the standard formulation for re-identification that are more relevant for mobile reidentification. We introduce the first dataset for mobile re-identification, and we use this to elucidate the unique challenges of mobile re-identification. Finally, we re-evaluate some conventional wisdom about re-id models in the light of these challenges and suggest future avenues for research in this area.

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Human Reidentification with Transferred Metric Learning

Cited by 423 publications

References 28 publications

A Two-Stage Approach for Bag Detection in Pedestrian Images

A Two-Stage Approach for Bag Detection in Pedestrian Images

Person re-identification with mutual re-ranking

Investigating Open-World Person Re-identification Using a Drone

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