The HDA+ Data Set for Research on Fully Automated Re-identification Systems

Figueira, Dario; Taiana, Matteo; Nambiar, Athira; Nascimento, Jacinto C.; Bernardino, Alexandre

doi:10.1007/978-3-319-16199-0_17

Cited by 19 publications

(15 citation statements)

References 14 publications

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“…Datasets Extensive experiments were conducted on three image sequence datasets designed for person ReID, iLIDS Video re-IDentification (iLIDS-VID) [53], PRID2011 [54], and HDA+ [55]. All three datasets are very challenging due to clothing similarities among people, lighting and viewpoint variations across camera views, cluttered background and occlusions ( Fig.…”

Section: Methodsmentioning

confidence: 99%

Person Re-Identification by Discriminative Selection in Video Ranking

Wang

Gong

Zhu

et al. 2016

IEEE Trans. Pattern Anal. Mach. Intell.

225

152

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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.

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Section: Methodsmentioning

confidence: 99%

Person Re-Identification by Discriminative Selection in Video Ranking

Wang

Gong

Zhu

et al. 2016

IEEE Trans. Pattern Anal. Mach. Intell.

225

152

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“…Also, since we are removing any frame that does not contain a foreground object, the resulting video will always be lesser in size than the original one. For comparison, we submitted the image sequences from various camera feeds from the HDA+ Person Dataset [8] to our topology and noted the size of the output. One of the Image sequences in the same dataset, (Camera18) consisted of 9882 frames, taking up 83.8MB of disk space.…”

Section: Resultsmentioning

confidence: 99%

A scalable and robust framework for intelligent real-time video surveillance

Dutt

Kalra

2016

2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

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In this paper, we present an intelligent, reliable and storage-efficient video surveillance system using Apache Storm and OpenCV. As a Storm topology, we have added multiple information extraction modules that only write important content to the disk. Our topology is extensible, capable of adding novel algorithms as per the use case without affecting the existing ones, since all the processing is independent of each other. This framework is also highly scalable and fault tolerant, which makes it a best option for organisations that need to monitor a large network of surveillance cameras.

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“…iLIDSVID [28] includes 600 tracking sequences for 300 people from 2 non-overlapping cameras in an airport and suffers from viewpoint and illumination variations, background clutter, and occlusions. HDA+ [82] was proposed to be a testbed for an automatic re-id system. Fully labeled frames for 30-minute long videos from 13 disjoint cameras are provided.…”

Section: Datasetsmentioning

confidence: 99%

A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets

Karanam

Gou

et al. 2019

IEEE Trans. Pattern Anal. Mach. Intell.

192

138

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Person re-identification (re-id) is a critical problem in video analytics applications such as security and surveillance. The public release of several datasets and code for vision algorithms has facilitated rapid progress in this area over the last few years. However, directly comparing re-id algorithms reported in the literature has become difficult since a wide variety of features, experimental protocols, and evaluation metrics are employed. In order to address this need, we present an extensive review and performance evaluation of single- and multi-shot re-id algorithms. The experimental protocol incorporates the most recent advances in both feature extraction and metric learning. To ensure a fair comparison, all of the approaches were implemented using a unified code library that includes 11 feature extraction algorithms and 22 metric learning and ranking techniques. All approaches were evaluated using a new large-scale dataset that closely mimics a real-world problem setting, in addition to 16 other publicly available datasets: VIPeR, GRID, CAVIAR, DukeMTMC4ReID, 3DPeS, PRID, V47, WARD, SAIVT-SoftBio, CUHK01, CHUK02, CUHK03, RAiD, iLIDSVID, HDA+ and Market1501. The evaluation codebase and results will be made publicly available for community use.

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The HDA+ Data Set for Research on Fully Automated Re-identification Systems

Cited by 19 publications

References 14 publications

Person Re-Identification by Discriminative Selection in Video Ranking

Person Re-Identification by Discriminative Selection in Video Ranking

A scalable and robust framework for intelligent real-time video surveillance

A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets

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