Search

• A novel top-down framework is presented to address multi-person pose estimation. • We improved the faster R-CNN detector by infusing the soft-NMS and ASDN items. • Heatmaps instead of activation map are used to pinpoint the precise joints location. Human motion recognition based on computer vision plays an important role in many fields, such as video surveillance, virtual reality, and medical care. To solve the inaccurate multi-person pose estimation problem and improve the generalizability of the extracted features, this paper proposes a multi-person pose estimation method based on a deep convolutional neural network. This method mainly relies on a top-down structure which includes two stages. In the first stage, the bounding boxes that are likely to contain people are first detected by an improved faster R-CNN. Individuals in the complex scenario are then tailored by box cropping. In the second stage, we combine heatmap detection with coordinate regression to address the single person pose estimation problem. Specially, a deep convolutional ResNet is employed to produce heatmaps of human body. The precise location of each joint is achieved by the fully connected conditional random field. Experimental results demonstrate our method achieves comparable performance with the state-of-the-art ones. [ABSTRACT FROM AUTHOR]

• We propose spectral pyramid transform to model actions sufficiently. • We design a sampling strategy to make actions with various duration aligned. • We propose a feature pyramid to fuse CNN features and spectral features. In this paper, we propose a novel Deep Spectral Feature Pyramid in the Frequency domain (DSFP) to share the merits of deep features and spectral approaches for long-term action recognition. More specifically, in the spatial domain, deep features of sparse sampled frames are extracted by Convolutional Neural Networks (CNNs) to cover long-term temporal structure. In the frequency domain, appearance features of sampled frames are partitioned recursively along the time dimension and spectral transform is applied to each partitioned feature respectively. All coefficients of partitioned features are then concatenated into a video-level feature to better model the spatio-temporal structure of actions in the form of a pyramid. So DSFP could model actions from both microcosmic and macroscopic aspects. Extensive experiments conducted on two challenging action benchmarks UCF101 and HMDB51 show that our proposed DSFP is effective for spatio-temporal representation of actions and achieves comparable performance with the state-of-the-arts. [ABSTRACT FROM AUTHOR]