Due to the availability of large-scale skeleton datasets, 3D human action recognition has recently called the attention of computer vision community. Many works have focused on encoding skeleton data as skeleton image representations based on spatial structure of the skeleton joints, in which the temporal dynamics of the sequence is encoded as variations in columns and the spatial structure of each frame is represented as rows of a matrix. To further improve such representations, we introduce a novel skeleton image representation to be used as input of Convolutional Neural Networks (CNNs), named SkeleMotion. The proposed approach encodes the temporal dynamics by explicitly computing the magnitude and orientation values of the skeleton joints. Different temporal scales are employed to compute motion values to aggregate more temporal dynamics to the representation making it able to capture longrange joint interactions involved in actions as well as filtering noisy motion values. Experimental results demonstrate the effectiveness of the proposed representation on 3D action recognition outperforming the state-of-the-art on NTU RGB+D 120 dataset.
In the last years, the computer vision research community has studied on how to model temporal dynamics in videos to employ 3D human action recognition. To that end, two main baseline approaches have been researched: (i) Recurrent Neural Networks (RNNs) with Long-Short Term Memory (LSTM); and (ii) skeleton image representations used as input to a Convolutional Neural Network (CNN). Although RNN approaches present excellent results, such methods lack the ability to efficiently learn the spatial relations between the skeleton joints. On the other hand, the representations used to feed CNN approaches present the advantage of having the natural ability of learning structural information from 2D arrays (i.e., they learn spatial relations from the skeleton joints). To further improve such representations, we introduce the Tree Structure Reference Joints Image (TSRJI), a novel skeleton image representation to be used as input to CNNs. The proposed representation has the advantage of combining the use of reference joints and a tree structure skeleton. While the former incorporates different spatial relationships between the joints, the latter preserves important spatial relations by traversing a skeleton tree with a depth-first order algorithm. Experimental results demonstrate the effectiveness of the proposed representation for 3D action recognition on two datasets achieving state-of-the-art results on the recent NTU RGB+D 120 dataset.
Binary descriptors have recently become very popular in visual recognition tasks. This popularity is largely due to their low complexity and for presenting similar performances when compared to non binary descriptors, like SIFT. In literature, many researchers have applied binary descriptors in conjunction with mid-level representations (e.g., Bag-ofWords). However, despite these works have demonstrated promising results, their main problems are due to use of a simple mid-level representation and the use of binary descriptors in which rotation and scale invariance are missing. In order to address those problems, we propose to evaluate state-of-the-art binary descriptors, namely BRIEF, ORB, BRISK and FREAK, in a recent mid-level representation, namely BossaNova, which enriches the Bag-of-Words model, while preserving the binary descriptor information. Our experiments carried out in the challenging PASCAL VOC 2007 dataset revealed outstanding performances. Also, our approach shows good results in the challenging real-world application of pornography detection.
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