Action parsing in videos with complex scenes is an interesting but challenging task in computer vision. In this paper, we propose a generic 3D convolutional neural network in a multi-task learning manner for effective Deep Action Parsing (DAP3D-Net) in videos. Particularly, in the training phase, action localization, classification and attributes learning can be jointly optimized on our appearancemotion data via DAP3D-Net. For an upcoming test video, we can describe each individual action in the video simultaneously as: Where the action occurs, What the action is and How the action is performed. To well demonstrate the effectiveness of the proposed DAP3D-Net, we also contribute a new Numerous-category Aligned Synthetic Action dataset, i.e., NASA, which consists of 200; 000 action clips of more than 300 categories and with 33 pre-defined action attributes in two hierarchical levels (i.e., low-level attributes of basic body part movements and high-level attributes related to action motion). We learn DAP3D-Net using the NASA dataset and then evaluate it on our collected Human Action Understanding (HAU) dataset. Experimental results show that our approach can accurately localize, categorize and describe multiple actions in realistic videos.
Since little work has been done on action parsing in videos for simultaneously solving the problems of action localization, categorization and attributes learning, in this section, we mainly review some related work on action detection and action attributes modeling. Action detection can be regarded as a combination of action localization and categorization. In @cite_29 , a weakly supervised model with multiple instance learning was applied for action detection. In @cite_60 , a dynamic-poselets method was introduced. Branch-and-bound algorithm @cite_48 was proposed to reduce the action detection complexity. There also exist some sub-volume @cite_30 @cite_3 @cite_51 based action detection methods. Besides, the cross-dataset action detection @cite_31 and the spatio-temporal deformable part models based action detection @cite_49 have also been proposed in previous studies. Additionally, action detection via fast proposals was developed in @cite_11 .
@cite_30 @cite_60 @cite_48 @cite_29 @cite_3 @cite_49 @cite_31 @cite_51 @cite_11
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Action detection is of great importance in understanding human motion from video. Compared with action recognition, it not only recognizes action type, but also localizes its spatiotemporal extent. This paper presents a relational model for action detection, which first decomposes human action into temporal “key poses” and then further into spatial “action parts”. Specifically, we start by clustering cuboids around each human joint into dynamic-poselets using a new descriptor. The cuboids from the same cluster share consistent geometric and dynamic structure, and each cluster acts as a mixture of body parts. We then propose a sequential skeleton model to capture the relations among dynamic-poselets. This model unifies the tasks of learning the composites of mixture dynamic-poselets, the spatiotemporal structures of action parts, and the local model for each action part in a single framework. Our model not only allows to localize the action in a video stream, but also enables a detailed pose estimation of an actor. We formulate the model learning problem in a structured SVM framework and speed up model inference by dynamic programming. We conduct experiments on three challenging action detection datasets: the MSR-II dataset, the UCF Sports dataset, and the JHMDB dataset. The results show that our method achieves superior performance to the state-of-the-art methods on these datasets. We consider the problem of detecting and localizing a human action from continuous action video from depth cameras. We believe that this problem is more challenging than the problem of traditional action recognition as we do not have the information about the starting and ending frames of an action class. Another challenge which makes the problem difficult, is the latency in detection of actions. In this paper, we introduce a greedy approach to detect the action class, invariant of their temporal scale in the testing sequences using class templates and basic skeleton based feature representation from the depth stream data generated using Microsoft Kinect. We evaluate the proposed method on the standard G3D and UTKinect-Action datasets consisting of five and ten actions, respectively. Our results demonstrate that the proposed approach performs well for action detection and recognition under different temporal scales, and is able to outperform the state of the art methods at low latency. In this work, we propose an approach to the spatiotemporal localisation (detection) and classification of multiple concurrent actions within temporally untrimmed videos. Our framework is composed of three stages. In stage 1, appearance and motion detection networks are employed to localise and score actions from colour images and optical flow. In stage 2, the appearance network detections are boosted by combining them with the motion detection scores, in proportion to their respective spatial overlap. In stage 3, sequences of detection boxes most likely to be associated with a single action instance, called action tubes, are constructed by solving two energy maximisation problems via dynamic programming. While in the first pass, action paths spanning the whole video are built by linking detection boxes over time using their class-specific scores and their spatial overlap, in the second pass, temporal trimming is performed by ensuring label consistency for all constituting detection boxes. We demonstrate the performance of our algorithm on the challenging UCF101, J-HMDB-21 and LIRIS-HARL datasets, achieving new state-of-the-art results across the board and significantly increasing detection speed at test time. We achieve a huge leap forward in action detection performance and report a 20 and 11 gain in mAP (mean average precision) on UCF-101 and J-HMDB-21 datasets respectively when compared to the state-of-the-art. Action recognition has often been posed as a classification problem, which assumes that a video sequence only have one action class label and different actions are independent. However, a single human body can perform multiple concurrent actions at the same time, and different actions interact with each other. This paper proposes a concurrent action detection model where the action detection is formulated as a structural prediction problem. In this model, an interval in a video sequence can be described by multiple action labels. An detected action interval is determined both by the unary local detector and the relations with other actions. We use a wavelet feature to represent the action sequence, and design a composite temporal logic descriptor to describe the action relations. The model parameters are trained by structural SVM learning. Given a long video sequence, a sequential decision window search algorithm is designed to detect the actions. Experiments on our new collected concurrent action dataset demonstrate the strength of our method.