ACML-TAB2019
Date: 2019/Nov/17 (Sun)
Venue: WINC AICHI
Place: Room 1103 (11th floor) or orals, 6th floor for posters
Submission deadline: 2019/Sep/30 --> 2019/Oct/10
ACML2019 workshop on Machine Learning for Trajectory, Activity, and Behavior (ACML-TAB)
Sunday 17th, November 2019
in conjunction with The 11th Asian Conference on Machine Learning (ACML2019), Nagoya, Japan
About
Recent advances in sensing technology have made it possible to collect vast amounts of trajectories, activities and behavior data from humans, animals, and vehicles. Smart devices and visual tracking are used to capture the data of players in the sports scene and vehicles in the city for skill assessment or resource allocations. Small GPS and acceleration loggers collect behavioral data from animals in the wild, such as birds and bats, to better understand the ecology of animals. Therefore, machine learning techniques have been developed to recognize, analyze, and predict the trajectory, activity, and behavior of various targets.
This workshop provides a place for engineers, computer scientists, biologist, and neuroscientists to discuss machine learning and related methods for trajectory, activity, and behavior data collected from various sources, such as humans, animals, insects, and automobiles. The topics of interest include, but are not limited to:
Machine learning, time series analysis, data mining, and knowledge extraction for trajectory/activity/behavior data
Modeling, collecting, data preparation and labeling for trajectory/activity/behavior data
Systems and applications of monitoring and recognition systems for trajectory/activity/behavior data
Localization, recognition, prediction, and visualization for trajectory/activity/behavior data
Submission
This workshop invites submissions of one or two page abstracts in PDF format for oral or poster presentations.
Submission deadline: 2019/Sep/30 --> 2019/Oct/10
Workshop date: 2019/Nov/17 (Sun)
There will be no published proceedings, and the submissions will be posted on the workshop website (and now all paper PDF is available in this page). All submissions, if relevant to the topics, are welcome to present in a poster session. (there is no reviewing, but organizers will select in case of many submissions or out-of-scope.)
Submission your abstract at EasyChair.
Author Kit
Submit a PDF file by using LaTeX or MS Word via the following templates:
Registration
Attendees and presenters of this workshop needs registrations for ACML2019. Follow the instruction in the link below:
Presentation instruction
For poster presentation
- The poster size should be A0 vertical (1189mm height x 841mm width); horizontal one cannot be displayed.
- Please check your poster board number at http://www.acml-conf.org/2019/workshops/posters/
- Put the poster board during the lunch time (12:30-13:30) on Nov. 17, and take if off when you leave the conference venue on Nov. 17.
For oral presentation
- Bring your laptop.
- 15min each including QA.
Timetable
Place: Room 1103 (11th floor); see ACML2019 program for other info
10:00-10:05: Opening
10:05-10:35: Invited talk 1: Hisashi Murakami, Self-organization of human crowds and animal groups driven by inherent noise
10:35-10:45: break
10:45-11:45: Oral session
11:45-11:50: break
11:50-12:20: Invited talk 2: Eijiro Takeuchi, Ways for mobile robot to go to destination
16:00-18:00: Poster session (6th floor) with coffee break
Invited Talks
Dr. Hisashi Murakami (Research Center for Advanced Science and Technology, The University of Tokyo, Japan)
Title: Self-organization of human crowds and animal groups driven by inherent noise
Abstract: Collective animal behavior is a paradigmatic example of self-organization in living systems. Although individual noisy movements seem to collapse the global order, they can be compatible with and rather facilitate dynamic collective behavior of the whole group. Here, through experiments and video tracking, we show that such movements in both human crowds and fish schools appear as a scale-free movement strategy called Lévy walk, which is considered an optimal strategy when searching unpredictably distributed resources. This suggests that by seeking clear passages through a group and/or interacting with various neighbors, a noise would be generated inherently in collective groups, which facilitate efficient transition to the group-level behavior, activating self-organization of the whole group.
Dr. Eijiro Takeuchi (Graduate School of Informatics, Nagoya University, Japan)
Title: Ways for mobile robot to go to destination
Abstract: Nowadays, autonomous vehicles are actively developed and steadily approaching commercialization. Such autonomous vehicles realize point to point navigation using highly integrated mobile robot technologies. This presentation introduces fundamental components of mobile robots such as localization, recognition, planning, and control, and how to realize point to point navigation using these functions. Almost these methods are designed by physical model. On the other hand, recently, so many deep learning solutions for navigation problems are proposed. This presentation discusses relationship between recent deep learning solutions for autonomous navigation and traditional navigation functions of mobile robots.Oral session 10:45-11:45 (15min each including QA)
3 Duy Nguyen Le Vo, Takuto Sakuma, Taiju Ishiyama, Hiroki Toda, Kazuya Arai, Masayuki Karasuyama, Yuta Okubo, Masayuki Sunaga, Yasuo Tabei and Ichiro Takeuchi. Statistical Significance of Discriminative Sub-trajectory
9 Hao Niu, Kei Yonekawa, Mori Kurokawa, Shinya Wada and Kiyohito Yoshihara. Transferable Representation Learning for Human Activity Recognition in Smart Homes
11 Yiming Tian, Takuya Maekawa, Joseph Korpela, Daichi Amagata, Takahiro Hara, Sakiko Matsumoto and Ken Yoda. Preliminary investigation of co-occurrence rule extraction for sex-specific behavior of Streaked Shearwater
14 Sandeep Nayak, Kazunori Ohno and Satoshi Tadokoro. Autonomous Navigation Guidance for Human through wearable light StimuliPoster session 16:00-18:00
1 Keisuke Fujii, Naoya Takeishi and Yoshinobu Kawahara. Interpretable classification of complex collective motions using graph dynamic mode decomposition
2 Kazushi Tsutsui, Keisuke Fujii and Kazuya Takeda. Data-driven modeling of locomotor behaviors in game-based chase and escape interactions
3 Duy Nguyen Le Vo, Takuto Sakuma, Taiju Ishiyama, Hiroki Toda, Kazuya Arai, Masayuki Karasuyama, Yuta Okubo, Masayuki Sunaga, Yasuo Tabei and Ichiro Takeuchi. Statistical Significance of Discriminative Sub-trajectory
4 Ryota Tomonaka, Toru Tamaki, Bisser Raytchev, Kazufumi Kaneda and Ken Yoda. On Trajectory Interpolation using LSTM
5 Kazuki Fujimori, Bisser Raytchev, Kazufumi Kaneda, Emyo Fujioka, Shizuko Hiryu and Toru Tamaki. Position estimation using multi-channel audio signals
6 Chentao Wen and Kotaro Kimura. Analyzing whole brain activities of a worm using data-driven models
7 Ryota Shimizu, Takahiro Uchiya and Ichi Takumi. Congestion Mitigation Verification using a Theme Park Guide Schedule
8 Yukihiro Achiha, Tsubasa Hirakawa, Takayoshi Yamashita and Hironobu Fujiyoshi. Flow Histogram-based Recurrent Neural Network for Visual Odometry Estimation
9 Hao Niu, Kei Yonekawa, Mori Kurokawa, Shinya Wada and Kiyohito Yoshihara. Transferable Representation Learning for Human Activity Recognition in Smart Homes
10 Shiho Koyama, Yuichi Mizutani and Ken Yoda. Unraveling the foraging strategies of breeding seabirds by combining trajectory, activity, and physiology
11 Yiming Tian, Takuya Maekawa, Joseph Korpela, Daichi Amagata, Takahiro Hara, Sakiko Matsumoto and Ken Yoda. Preliminary investigation of co-occurrence rule extraction for sex-specific behavior of Streaked Shearwater
12 Javier Zazo, Melanie F. Pradier and Santiago Zazo. Distributed Non-Convex Least Squares Localization Problem
13 Tiwat Larpvisuttisaroj and Koichi Hashimoto. Deep Neural Network for Estimating Bat Pose
14 Sandeep Nayak, Kazunori Ohno and Satoshi Tadokoro. Autonomous Navigation Guidance for Human through wearable light Stimuli
15 Shintaro Takayama, Masao Kuwahara, Yosuke Kawasaki, Shogo Umeda and Koichi Hashimoto. Vehicle anormality evaluation using probe trajectory data
16 Yasutaka Furusho and Kazushi Ikeda. Generation and Visualization of Tennis Swing Motion by Conditional Variational RNN with Hidden Markov Model
17 Shinsuke Kajioka, Takuto Sakuma and Ichiro Takeuchi. Comparative Sequential Pattern Mining of Human Trajectory Obtained from BLE Beacons Collected by Smartphones18 Ranulfo Bezerra, Kazunori Ohno, Thomas Westfechtel and Satoshi Tadokoro. Pedestrian Flow Estimation Using Sparse Observation from Autonomous Vehicles
Organizers
members of KAKENHI project "Systems Science of Bio-Navigation"; see more detail at our website
Systems Science of Bio-Navigation
Grant-in-Aid for Scientific Research on Innovative Areas from 2016 to 2021
Navigation is a fundamental behavior of animals including human. In navigation, the following three functions are required: the acquisition of dynamically-changing information from external and internal environment, the choice of route and destination based on the information, and the behavioral regulation to reach the destination. We aim for systems science of bio-navigation to understand the “algorithms” for the navigation of animals. To this end, we bring together experts from control engineering, data science, animal ecology, and neuroscience, and jointly work on how to measure, analyze, understand, and verify bio-navigation.
Please visit our website.
© 2019