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Visual human motion understanding in the Wild

Visual human motion understanding is a key computer vision task that aims at understanding the placement, trajectories, and future actions of humans in a unconstrained natural scene. This field includes the key tasks of finding people in a scene through people detection, segmentation and pose estimation, understanding movement through people tracking, and recognizing people behaviors through motion trajectories. Recently, there has been increasing interest from the academic vision community about this topic, as well as from communities in industry due to its applications in a number of fields. For example, safe mobile robot navigation, including autonomous driving depends on robots (cars) being able to recognize where nearby pedestrians are and what they might do next. Likewise, human motion understanding is key for smart surveillance, athletic performance analysis, and VR applications, human computer interaction, among others.

Edited by Shengping Zhang, Huiyu Zhou, Xiangyuan Lan, Lei Zhang, Christophoros Nikou

  1. We present a novel approach to non-rigid object tracking in this paper by deriving an adaptive data-driven kernel. In contrast with conventional kernel-based trackers which suffer from the constancy of kernel ...

    Authors: Xin Sun, Wei Wang, Dong Li, Bin Zou and Hongxun Yao
    Citation: EURASIP Journal on Advances in Signal Processing 2020 2020:9
  2. The person re-identification is one of the most significant problems in computer vision and surveillance systems. The recent success of deep convolutional neural networks in image classification has inspired r...

    Authors: Aleksei Grigorev, Zhihong Tian, Seungmin Rho, Jianxin Xiong, Shaohui Liu and Feng Jiang
    Citation: EURASIP Journal on Advances in Signal Processing 2019 2019:54
  3. In this paper, online non-negative discriminative dictionary learning for tracking is proposed, which combines the advantages of the global dictionary learning model and the class-specific dictionary learning ...

    Authors: Weisong Wang, Fei Yang and Hongzhi Zhang
    Citation: EURASIP Journal on Advances in Signal Processing 2019 2019:50
  4. A robust object tracking algorithm is proposed in this paper based on an online discriminative appearance modeling mechanism. In contrast with traditional trackers whose computations cover the whole target reg...

    Authors: Wei Liu, Xin Sun and Dong Li
    Citation: EURASIP Journal on Advances in Signal Processing 2019 2019:48
  5. In recent years, deep convolutional neural networks (CNNs) have achieved great success in visual tracking. To learn discriminative representations, most of existing methods utilize information of image region ...

    Authors: Heyan Zhu and Hui Wang
    Citation: EURASIP Journal on Advances in Signal Processing 2019 2019:41
  6. Video summarization aims to provide a compact video representation while preserving the essential activities of the original video. Most existing video summarization approaches relay on identifying important f...

    Authors: Gang Pan, Yaoxian Zheng, Rufei Zhang, Zhenjun Han, Di Sun and Xingming Qu
    Citation: EURASIP Journal on Advances in Signal Processing 2019 2019:15