3D Human Motion Indexing and Retrieval is an interesting problem due to the rise of several data-driven applications aimed at analyzing and or re-utilizing 3D human skelet al data, such as data-driven animation, analysis of sports bio-mechanics, human surveillance etc. Spatio-temporal articulations of humans, noisy missing data, different speeds of the same motion etc. make it challenging and several of the existing state of the art methods use hand-craft features along with optimization based or histogram based comparison in order to perform retrieval. Further, they demonstrate it only for very small datasets and few classes. We make a case for using a learned representation that should recognize the motion as well as enforce a discriminative ranking. To that end, we propose, a 3D human motion descriptor learned using a deep network. Our learned embedding is generalizable and applicable to real-world data - addressing the aforementioned challenges and further enables sub-motion searching in its embedding space using another network. Our model exploits the inter-class similarity using trajectory cues, and performs far superior in a self-supervised setting. State of the art results on all these fronts is shown on two large scale 3D human motion datasets - NTU RGB+D and HDM05.
Most approaches on the 3D human motion retrieval have focused on developing hand crafted features to represent the skeleton sequences @cite_17 @cite_19 @cite_4 . In this section, we broadly categorize them by the method in which they engineer their descriptors.
@cite_19 @cite_4 @cite_17
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Human motion retrieval plays an important role in many motion data based applications. In the past, many researchers tended to use a single type of visual feature as data representation. Because different visual feature describes different aspects about motion data, and they have dissimilar discriminative power with respect to one particular class of human motion, it led to poor retrieval performance. Thus, it would be beneficial to combine multiple visual features together for motion data representation. In this article, we present an Adaptive Multi-view Feature Selection (AMFS) method for human motion retrieval. Specifically, we first use a local linear regression model to automatically learn multiple view-based Laplacian graphs for preserving the local geometric structure of motion data. Then, these graphs are combined together with a non-negative view-weight vector to exploit the complementary information between different features. Finally, in order to discard the redundant and irrelevant feature components from the original high-dimensional feature representation, we formulate the objective function of AMFS as a general trace ratio optimization problem, and design an effective algorithm to solve the corresponding optimization problem. Extensive experiments on two public human motion database, i.e., HDM05 and MSR Action3D, demonstrate the effectiveness of the proposed AMFS over the state-of-art methods for motion data retrieval. The scalability with large motion dataset, and insensitivity with the algorithm parameters, make our method can be widely used in real-world applications. Display Omitted An Adaptive Multi-view Feature Selection (AMFS) algorithm is proposed to fuse multiple features formotion data retrieval.The local regression model isused to learn a datum-adaptive graph for each feature to preserve local structure information.The selection matrix is learnt from all local graphs by exploiting the complementary information between different features.An efficient iterative optimization approach is designed to solve objective function represented in trace ratio form. Human action recognition is an important yet challenging task. Human actions usually involve human-object interactions, highly articulated motions, high intra-class variations, and complicated temporal structures. The recently developed commodity depth sensors open up new possibilities of dealing with this problem by providing 3D depth data of the scene. This information not only facilitates a rather powerful human motion capturing technique, but also makes it possible to efficiently model human-object interactions and intra-class variations. In this paper, we propose to characterize the human actions with a novel actionlet ensemble model, which represents the interaction of a subset of human joints. The proposed model is robust to noise, invariant to translational and temporal misalignment, and capable of characterizing both the human motion and the human-object interactions. We evaluate the proposed approach on three challenging action recognition datasets captured by Kinect devices, a multiview action recognition dataset captured with Kinect device, and a dataset captured by a motion capture system. The experimental evaluations show that the proposed approach achieves superior performance to the state-of-the-art algorithms. In this paper, we propose a content-based motion retrieval algorithm. In this work, firstly, each motion is represented by a set of sequence frames. Representative frames are first selected from the motions and the corresponding weights are provided. Secondly, the graph model is built with these selected frames. For searching the optimal measurement between query motion and relevant motions in database, an object function is built. The task to find the maximal a posterior (MAP) in the motion level is equivalent to find the minimal objective function value. At last, based on probability calculation, the KM (Kuhn-Munkres) algorithm is used to find the optimal matching between motions. The matching result is used to measure the similarity between two motions. Experimental results and comparison with existing methods show the effectiveness of the proposed algorithm.