[image 00930] 講演会のご案内（12月8日(月) 12:55-14:25@大阪府立大学 B4棟 西K-301室）

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ダッカ大学の Md. Atiqur Rahman Ahad 准教授をお迎えして、
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岩田　基
大阪府立大学 大学院 工学研究科
知能情報工学分野 第3グループ　助教
〒599-8531　大阪府堺市中区学園町1-1
TEL/FAX: 072-254-9281　　内線6805

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日時　：2014年12月8日（月）　12:55-14:25
場所　：大阪府立大学　B4棟 3階 西K-301室
講師　：Md. Atiqur Rahman Ahad 准教授
         Department of Applied Physics, Electronics & Communication Engineering,
         University of Dhaka, Bangladesh
         Personal site: http://aa.binbd.com
         Email: atiqahad ＠ du.ac.bd
主催：　大阪府立大学　文書解析・知識科学研究所 (IDAKS)

講演タイトル：
Challenges and Constraints of Action Analysis

講演概要：
What constitutes an action or activity is difficult to define, as there is
no clear distinctive nomenclature on this [1-2]. However, an action refers
to a simple, atomic movement performed by a single person; whereas an activity
denotes a more complex scenario that involves a group of people [3].
Action/activity analysis, recognition, understanding from video sequences
have various applications in HCI, man-machine interaction, biomechanics,
robotics, surveillance, games, etc.

Human action analysis is a challenging problem due to large variations in
human motion and appearance, camera viewpoint and settings [4]. Some important
but common motion recognition problems remain unsolved properly, even though
a number of good approaches are proposed and evaluated.

The challenges are manifold. One important area is stereo vision and stereo
reconstruction (especially to understand pedestrian's activity analysis).
For action recognition in complex scenes, dimensional variations matter a lot [1].
Few diverse areas are - static vs. dynamic scenes; multi-objects' identification
or recognition; analyzing the context of scene; goal-directed behavior analysis.
These issues can be handled for action understanding. In order to understand
any goal-directed behavior, we need to analyze the main context of the scenes
apart from just recognizing objects. It is important to relate the objects with
action verbs. It is also necessary to consider action-context, which defines
an action based on the various actions or activities around the person of interest
in a scene having multiple subjects. So far, we are dealing with familiar actions
and activities. However, there are high demands for understanding various unfamiliar
activities, especially for video surveillance-related applications. It is required
to know or predict what is going to happen next based on the current understanding
of the actions.

Understanding scene and its contexts are crucial to understand an action. We need
some new and challenging datasets for action or activity analysis. We need to
adopt diversity and dimensions to develop new datasets. There are various other
challenging aspects and some of these are very difficult to address based on the
present progresses on action understanding; hence, these issues require time.
The better and the robust approaches we have, the more realistic wider applications
we can address in future.
   
Reference:
1. Md. Atiqur Rahman Ahad, "Computer Vision and Action Recognition: A Guide for
    Image Processing and Computer Vision Community for Action Understanding",
    Atlantis / Springer, 2011.
2. Md. Atiqur Rahman Ahad, "Motion History Images for Action Recognition and Understanding",
    Springer, 2012.
3. T. Lan, Y. Wang, W. Yang, D: Mori, "Beyond Actions: Discriminative Models for
    Contextual group activities", Neural Information Processing Systems (NIPS).
4. R. Poppe, "Vision-based human motion analysis: an overview" Computer Vision and
    Image Understanding 2007, 108(1-2):4-18.
5. Md. Atiqur Rahman Ahad, J Tan, H Kim, S Ishikawa, "Motion History Image:
    Its Variants and Applications", Machine Vision and Applications, Vol. 23, No. 2,
    pp. 255-281, 2012.