Call for papers of a special issue on Deep Video Analysis: Models, Algorithms and Applications
• 大类 : 工程技术 - 2区
• 小类 : 计算机：人工智能 - 2区
• 小类 : 工程：电子与电气 - 2区
Video analysis is an important research area in pattern recognition and computer vision. The past decades have witnessed the rapid expansion of the video data generated every day including video surveillance, personal mobile device capture, and webs upload. It is quite needed for understanding such a large amount of video data. Meanwhile, deep learning, as a fast-growing research field, showed a vast successover a wide of research areas. The combination of the big visual data and the deep learning paradigm would bring a significant progress in both video analysis and life-related applications. However, the current capability gap of deep learning to handle video analysis is still huge. This mainly because video data conveys rich spatial and temporal information. In addition, the annotation of video labels especially pixel-level labels is expensively-acquired, which limits the further learning of deep neural networks. Thus, there is a pressing demand for novel deep learning based video analysis approaches that can cope with video analysis task with better accuracy and efficiency. In this special issue, we attempt to assemble recent advances in the deep learning based video analysis and related extended applications.
This special issue will feature original research papers related to the models and algorithm for various video analysis tasks from the low-level (e.g., video processing) to high-level (e.g., spatiotemporal reasoning), together with widespread applications to real-world issues. The main topics of interest (but are not limited to):
— Learning data representation from video based on supervised/unsupervised/semi-supervised learning.
—Video object tracking and segmentation: 1) Single object/multiple objects tracking; 2) Video segmentation; 3) Object saliency prediction.
—Optical flow estimation and visual SLAM.
—Person re-identification, multiple-camera tracking and vehicle re-identification, and human pose estimation/tracking.
—Dynamic scene parsing and semantic video segmentation.
—Video understanding: 1) Video classification and spatiotemporal reasoning; 2) Video action recognition; 3) Video summarization/video captioning; 4) Video generation; 5) Video anomaly detection.
—Applications: Applications of the corresponding methods to solve real-world video analysis issues including robot visions, machine visions, video object detection etc.
—New benchmark datasets related to the aforementioned topics.