Human Activity Recognition in RGB-D videos has been an active research topic during the last decade. However, no efforts have been found in the literature, for recognizing human activity in RGB-D videos where several performers are performing simultaneously. In this paper we introduce such a challenging dataset with several performers performing the activities. We present a novel method for recognizing human activities in such videos. The proposed method aims in capturing the motion information of the whole video by producing a dynamic image corresponding to the input video. We use two parallel ResNext-101 to produce the dynamic images for the RGB video and depth video separately. The dynamic images contain only the motion information and hence, the unnecessary background information are eliminated. We send the two dynamic images extracted from the RGB and Depth videos respectively, through a fully connected layer of neural networks. The proposed dynamic image reduces the complexity of the recognition process by extracting a sparse matrix from a video. However, the proposed system maintains the required motion information for recognizing the activity. The proposed method has been tested on the MSR Action 3D dataset and has shown comparable performances with respect to the state-of-the-art. We also apply the proposed method on our own dataset, where the proposed method outperforms the state-of-the-art approaches.
Recognizing human activity in RGB-D videos is an active research area in computer vision and robotics @cite_16 . Most of the approaches for depth based activity recognition are based on handcrafted features ranging from gradient, optical flow, HOG, interest points, etc. Oreifej @cite_10 proposed Histogram of oriented 4D surface normals (HON4D), based on the assumption that, the normal orientation captures more information than the gradient orientation. The descriptors are acquired from 4D volumes for recognition. Spinello @cite_14 used HOG to find the objects. Then Histogram of Oriented Depth value (HOD) is used for extracting depth information. Rahmani @cite_23 proposed Histogram of Oriented Principal Components(HOPC) to tackle the issues related to viewpoint variations. Koch @cite_20 proposed a volumetric approach for finding the depth estimates for all pixels and projected in the voxelized 3D space. Each depth estimate votes for a voxel probabilistically and the surfaces are extracted by thresholding. The handcrafted features are often found unable to find the motion in depth sequences, due to the 2D representation of the depth map, available with the data.
@cite_14 @cite_23 @cite_16 @cite_10 @cite_20
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We present a new descriptor for activity recognition from videos acquired by a depth sensor. Previous descriptors mostly compute shape and motion features independently, thus, they often fail to capture the complex joint shape-motion cues at pixel-level. In contrast, we describe the depth sequence using a histogram capturing the distribution of the surface normal orientation in the 4D space of time, depth, and spatial coordinates. To build the histogram, we create 4D projectors, which quantize the 4D space and represent the possible directions for the 4D normal. We initialize the projectors using the vertices of a regular polychoron. Consequently, we refine the projectors using a discriminative density measure, such that additional projectors are induced in the directions where the 4D normals are more dense and discriminative. Through extensive experiments, we demonstrate that our descriptor better captures the joint shape-motion cues in the depth sequence, and thus outperforms the state-of-the-art on all relevant benchmarks. Human activity recognition based on RGB-D data has received more attention in recent years. We propose a spatiotemporal feature named three-dimensional (3D) sparse motion scale-invariant feature transform (SIFT) from RGB-D data for activity recognition. First, we build pyramids as scale space for each RGB and depth frame, and then use Shi-Tomasi corner detector and sparse optical flow to quickly detect and track robust keypoints around the motion pattern in the scale space. Subsequently, local patches around keypoints, which are extracted from RGB-D data, are used to build 3D gradient and motion spaces. Then SIFT-like descriptors are calculated on both 3D spaces, respectively. The proposed feature is invariant to scale, transition, and partial occlusions. More importantly, the running time of the proposed feature is fast so that it is well-suited for real-time applications. We have evaluated the proposed feature under a bag of words model on three public RGB-D datasets: one-shot learning Chalearn Gesture Dataset, Cornell Activity Dataset-60, and MSR Daily Activity 3D dataset. Experimental results show that the proposed feature outperforms other spatiotemporal features and are comparative to other state-of-the-art approaches, even though there is only one training sample for each class. In this paper, we address the problems of contour detection, bottom-up grouping, object detection and semantic segmentation on RGB-D data. We focus on the challenging setting of cluttered indoor scenes, and evaluate our approach on the recently introduced NYU-Depth V2 (NYUD2) dataset (, ECCV, 2012). We propose algorithms for object boundary detection and hierarchical segmentation that generalize the @math gPb-ucm approach of (TPAMI, 2011) by making effective use of depth information. We show that our system can label each contour with its type (depth, normal or albedo). We also propose a generic method for long-range amodal completion of surfaces and show its effectiveness in grouping. We train RGB-D object detectors by analyzing and computing histogram of oriented gradients on the depth image and using them with deformable part models (, TPAMI, 2010). We observe that this simple strategy for training object detectors significantly outperforms more complicated models in the literature. We then turn to the problem of semantic segmentation for which we propose an approach that classifies superpixels into the dominant object categories in the NYUD2 dataset. We design generic and class-specific features to encode the appearance and geometry of objects. We also show that additional features computed from RGB-D object detectors and scene classifiers further improves semantic segmentation accuracy. In all of these tasks, we report significant improvements over the state-of-the-art. Recognizing human activities from common color image sequences faces many challenges, such as complex backgrounds, camera motion, and illumination changes. In this paper, we propose a new 4-dimensional (4D) local spatio-temporal feature that combines both intensity and depth information. The feature detector applies separate filters along the 3D spatial dimensions and the 1D temporal dimension to detect a feature point. The feature descriptor then computes and concatenates the intensity and depth gradients within a 4D hyper cuboid, which is centered at the detected feature point, as a feature. For recognizing human activities, Latent Dirichlet Allocation with Gibbs sampling is used as the classifier. Experiments are performed on a newly created database that contains six human activities, each with 33 samples with complex variations. Experimental results demonstrate the promising performance of the proposed features for the task of human activity recognition.