Journal Menu► ▼ Journal Menu
Journal Browser► ▼ Journal Browser
Special Issue "Semantic Representations for Behavior Analysis in Robotic System"
Deadline for manuscript submissions: closed (30 June 2019).
Interests: robotic vision; path planning of UAVs; pattern recognition; machine learning; face recognition; wavelets
Interests: deep learning; video analysis; object tracking; wavelets
Interests: compressed sensing; signal and image processing; pattern recognition; computer vision; hyperspectral image analysis
Special Issues and Collections in MDPI journals
Special Issue in Remote Sensing: Dimensionality Reduction for Hyperspectral Imagery Analysis
Special Issue in Remote Sensing: Robust Multispectral/Hyperspectral Image Analysis and Classification
Special Issue in Remote Sensing: Joint Artificial Intelligence and Computer Vision Applications in Remote Sensing
Special Issue in Sensors: Sensors Signal Processing and Visual Computing 2019
Vision sensors and smart cameras have been increasingly used in a variety of applications, such as surveillance, manufacturing, entertainment, robotics, etc. Robotic vision plays a significant role in the era of artificial intelligence.
Among the related topics, motion and behavior analysis in robotic systems have witnessed tremendous progress in the last twenty years. Recently, researchers in robotic vision have shifted their attention from the monitoring of a single subject’ behaviors in a relatively simple environment to that of the behavior of multiple subjects in a crowd environment. In contrast to single subject behavior, multiple behavior analysis faces more challenges, such as complex interactions, diverse semantics and various expressions. This is due to the gap between the information directly extracted from videos and semantic interpretations by human beings.
To bridge this gap, a number of feature representation approaches (e.g., Cuboids, HOG/HOF, HOG3D, and eSURF) have been subsequently reported to address the coherence between the extracted features and the semantic interpretations. Unfortunately, due to redundancy and complexity, these hard-crafted features may lead to diverse variations in semantic representations for social behavior analysis.
In recent years, deep semantic representations have proven to be an effective tool for complicated behavior analysis. Such high-level semantic representations achieve desired performance even if in crowded environments. In addition, statistical approaches, syntactic approaches, and description-based approaches also gain increasing attention in computer vision community.
On the other hand, with the continuous deepening of space exploration, the research and development of space robot systems is becoming more and more important. By analyzing the space information obtained from a sensor system and visual system carried by the space robot, people can have more knowledge and judgments regarding space. Therefore, how to process the information obtained by space robots and how to transmit it to ground stations is also a very important question.
The primary purpose of this Special Issue is to organize a collection of recently-developed semantic representations for behavior analysis in complex environments, spreading over object detection, tracking, motion trajectory acquisition and analysis, semantic feature extraction, social behavior analysis and applications. This Special Issue is intended to be an international forum for researchers to report recent developments in this field. Topics include, but are not limited to:
- All aspects of robotic vision and UAVs
- Real-time moving object detection and tracking in crowed environments
- 3D scene reconstruction and occlusion handling
- Long-term trajectory clustering and analysis for crowed behaviors
- Probabilistic statistical models for local semantic representation
- Context model for global semantic representation
- Event recognition in crowed environments
- Abnormal behavior detection in crowed environments
- Real-time algorithms for large scale social behavior analysis
- Signal processing and communication system of space robot to ground station
- Deep learning for resource-constrained embedded vision sensor applications
Dr. Jungong Han
Dr. Chen Chen
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.