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Subject:
From:
Albert Clapés i Sintes <[log in to unmask]>
Reply To:
Albert Clapés i Sintes <[log in to unmask]>
Date:
Wed, 20 Nov 2019 15:22:07 +0100
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*** Please accept our apologies if you receive multiple copies of this CfP
***

This is a message regarding a change of status of our competition
*IEEE FG 2020 Chalearn Looking at People Challenge on Identity-preserved
Human Detection (IPHD)* at http://chalearnlap.cvc.uab.es/challenge
/34/description.

*The competition is now up and running!*

*CONTEST DESCRIPTION*

For the competition, we ask the participants to perform human detection in
depth and/or thermal images. Human detection in images/video is a
challenging computer vision problem with applications in human-computer
interaction, patient monitoring, surveillance, and autonomous driving, just
to mention a few. In some applications, however, keeping people's privacy
is a big concern for both users and companies/institutions involved. Most
notably, unintended identity revelation of subjects is perhaps the greatest
peril. While video data from RGB cameras are massively available to train
powerful detection models, the nature of these data may also allow
unpermitted third parties to access such data to try to identify observed
subjects. We argue that moving away from visual sensors that capture
identity information in the first place is the safest bet. However, the
lack of these more privacy-safe data affects the ability to train big
deep-learning models, thus affecting negatively the popularity of these
sensors.

For this competition, we offer a freshly-recorded multimodal image dataset
consisting of over 100K spatiotemporally aligned depth-thermal images of
different people recorded in public and private spaces: street, university
(cloister, hallways, and rooms), a research center, libraries, and private
houses. In particular, we used RealSense D435 for depth and FLIR Lepton v3
for thermal. Given the noisy nature of such commercial depth camera and the
thermal image resolution, the subjects are hardly identifiable. The dataset
contains a mix of close-range in-the-wild pedestrian scenes and indoor ones
with people performing in scripted scenarios, thus covering a larger space
of poses, clothing, illumination, background clutter, and occlusions. The
scripted scenarios include basic actions such as: sit on the sofa, lay on
the floor, interacting with kitchen appliances, cooking, eating, working on
the computer, talking on the phone, and so on. The camera position is not
necessarily static, but sometimes held by a person. The data were
originally collected as videos from different duration (from seconds to
hours) but skipping frames where no movement was observed. The ordering of
frames is removed to make it an image dataset (the only information
provided will be the video ID).

There are *three tracks* associated to this contest:

1. *Depth track*. Given the provided depth frames (and bounding box
groundtruth annotations), the participants will be asked to develop their
depth-based human detection method. Depth cameras are cost-effective
devices that provide geometric information of the scene at a resolution and
frame acquisition speed that is comparable to RGB cameras. The downside is
their noisiness at large real distances. The method developed by the
participants will need to output a list of bounding boxes (and their
confidence scores) per frame containing each person in it. The performance
on depth image-based human detection will be evaluated.

2. *Thermal track*. Given the provided thermal frames (and bounding box
groundtruth annotations), the participants will be asked to develop their
thermal-based human detection method. Thermal cameras provide temperature
readings from the scene. They are less noisy than depth cameras, but at a
comparable price they offer a much lower image resolution. The method
developed by the participants will need to output a list of bounding boxes
(and their confidence scores) per frame containing each person in it. The
performance on depth image-based human detection will be evaluated.

3. *Depth-Thermal Fusion track*. Given the provided aligned depth-thermal
frames (and bounding box groundtruth annotations), the participants will be
asked to develop their multimodal (depth and thermal) human detection
method. Both modalities have been temporally and spatially aligned and,
hence, so they will try to exploit their potential complementarity with a
proper fusion strategy. The participants will need to output a list of
bounding boxes per frame (and their confidence scores) containing each
person in it. The performance on depth image-based human detection will be
evaluated.

The competition will be run in the CodaLab platform. The participants will
register through the platform, where they will be able to access to the
different tracks (corresponding data, evaluation scripts, leaderboard, etc).

The CodaLab can be found at:
 http://chalearnlap.cvc.uab.es/challenge/34/description.


*ASSOCIATED EVENTS*
The participants will be invited to submit their papers to the associated
event:
*IEEE FG 2020 Workshop on Privacy-aware Computer Vision*,
http://chalearnlap.cvc.uab.es/workshop/35/description/

Accepted papers will be published within IEEE FG 2020 proceedings.


*IMPORTANT DATES*
- Start of the competition: November 19th, 2019
- Release of encrypted test data and validation groundtruth: January 22th,
2020
- Start of test phase: January 25th, 2020
- End of the quantitative competition: February 4th, 2020
- Fact sheets and material submission: February 8th, 2020
- Verification of results: February 8th, 2020

(Optionally, for those participants submitting papers to the associated
workshop)
- Paper submission deadline: February 15th, 2020
- Notification to authors: February 23th, 2020
- Camera-ready submission deadline: February 27th, 2020


*ORGANIZATION & SPONSORS*
Albert Clapés, Computer Vision Center at Universitat Autònoma de Barcelona
Carla Morral, Universitat de Barcelona
Julio C.S. Jacques Junior, Computer Vision Center at Universitat Autònoma
de Barcelona & Universitat Oberta de Catalunya
Sergio Escalera, Computer Vision Center at Universitat Autònoma de
Barcelona & Universitat de Barcelona

This event is sponsored by Chalearn.

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