The Re-identification Challenge

Gong, Shaogang; Cristani, Marco; Loy, Chen Change; Hospedales, Timothy M.

doi:10.1007/978-1-4471-6296-4_1

Cited by 126 publications

(108 citation statements)

References 53 publications

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“…Visual recognition of pedestrian attributes, such as gender, age, clothing style, is an emerging research topic in computer vision research, due to its high application potential in areas such as video-based business intelligence [16] and visual surveillance [5]. In many real-world surveillance scenarios, clear close-shots of face and body regions are not available.…”

Section: Introductionmentioning

confidence: 99%

Pedestrian Attribute Recognition At Far Distance

Deng

Luo

Loy

et al. 2014

Proceedings of the 22nd ACM International Conference on Multimedia

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376

277

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The capability of recognizing pedestrian attributes, such as gender and clothing style, at far distance, is of practical interest in far-view surveillance scenarios where face and body close-shots are hardly available. We make two contributions in this paper. First, we release a new pedestrian attribute dataset, which is by far the largest and most diverse of its kind. We show that the large-scale dataset facilitates the learning of robust attribute detectors with good generalization performance. Second, we present the benchmark performance by SVM-based method and propose an alternative approach that exploits context of neighboring pedestrian images for improved attribute inference.

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Section: Introductionmentioning

confidence: 99%

Pedestrian Attribute Recognition At Far Distance

Deng

Luo

Loy

et al. 2014

Proceedings of the 22nd ACM International Conference on Multimedia

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376

277

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“…Very recently, open-world re-id [46], [47], [48] has been introduced, where persons in each camera may be only partially overlapping and the number of cameras, spatial size of the environment, and number of people may be unknown and at a significantly larger scale. Recall that the goal of this paper is to identify the persons given aligned images, which are the cases in most person re-identification benchmark datasets, while open-world re-id this is more a system level concept that must deal with issues such as person detection, tracking, re-id, data association, etc.…”

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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“…The other major design axis typically considered is 'single-shot' [3,1,34] (exactly one image per person) versus 'multi-shot' [8,16,27] (exploiting multiple images per person where available to improve results). For a broad background of research to this paper we suggest [11] and [32]. Going beyond conventional re-identification, we next discuss a few recently identified research areas that are relevant to our MRP context.…”

Section: Related Workmentioning

confidence: 99%

“…This is significant because most of the recent performance gains in the state of the art re-id methods have come from supervised learning of view or view-pair specific models [11]. In the MRP case the continually varying view parameters -including range, lighting, self induced motion blur and detection alignmentprecludes learning such models (see Figure 1).…”

Section: Introductionmentioning

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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The Re-identification Challenge

Cited by 126 publications

References 53 publications

Pedestrian Attribute Recognition At Far Distance

Pedestrian Attribute Recognition At Far Distance

PRISM: Person Reidentification via Structured Matching

Investigating Open-World Person Re-identification Using a Drone

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