Most approaches to human attribute and action recognition in still images are based on image representation in which multi-scale local features are pooled across scale into a single, scale-invariant encoding. Both in bag-of-words and the recently popular representations based on convolutional neural networks, local features are computed at multiple scales. However, these multi-scale convolutional features are pooled into a single scale-invariant representation. We argue that entirely scale-invariant image representations are sub-optimal and investigate approaches to scale coding within a bag of deep features framework. Our approach encodes multi-scale information explicitly during the image encoding stage. We propose two strategies to encode multi-scale information explicitly in the final image representation. We validate our two scale coding techniques on five datasets: Willow, PASCAL VOC 2010, PASCAL VOC 2012, Stanford-40 and Human Attributes (HAT-27). On all datasets, the proposed scale coding approaches outperform both the scale-invariant method and the standard deep features of the same network. Further, combining our scale coding approaches with standard deep features leads to consistent improvement over the state of the art.
Action recognition in still images. Recognizing actions in still images is a difficult problem that has gained a lot of attention recently . In action recognition, bounding box information of each person instance is provided both at train and test time. The task is to associate an action category label with each person bounding box. Several approaches have addressed the problem of action recognition by finding human-object interactions in an image . A poselet-based approach was proposed in @cite_17 where poselet activation vectors capture the pose of a person. @cite_31 proposed a human-centric approach that localizes humans and objects associated with an action. @cite_24 propose to learn a set of sparse attribute and part bases for action recognition in still images. Recently, a comprehensive survey was performed by @cite_27 on action recognition methods exploiting semantic information. In their survey, it was shown that methods exploiting semantic information yield superior performance compared to their non-semantic counterparts in many scenarios. Human action recognition in still images is also discussed within the context of fuzzy domain in a recent survey .
@cite_24 @cite_27 @cite_31 @cite_17
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We focus on the problem of still image-based human action recognition, which essentially involves making prediction by analyzing human poses and their interaction with objects in the scene. Besides image-level action labels (e.g., riding, phoning), during both training and testing stages, existing works usually require additional input of human bounding boxes to facilitate the characterization of the underlying human–object interactions. We argue that this additional input requirement might severely discourage potential applications and is not very necessary. To this end, a systematic approach was developed in this paper to address this challenging problem of minimum annotation efforts, i.e., to perform recognition in the presence of only image-level action labels in the training stage. Experimental results on three benchmark data sets demonstrate that compared with the state-of-the-art methods that have privileged access to additional human bounding-box annotations, our approach achieves comparable or even superior recognition accuracy using only action annotations in training. Interestingly, as a by-product in many cases, our approach is able to segment out the precise regions of underlying human–object interactions. Recognition of human actions is usually addressed in the scope of video interpretation. Meanwhile, common human actions such as ''reading a book'', ''playing a guitar'' or ''writing notes'' also provide a natural description for many still images. In addition, some actions in video such as ''taking a photograph'' are static by their nature and may require recognition methods based on static cues only. Motivated by the potential impact of recognizing actions in still images and the little attention this problem has received in computer vision so far, we address recognition of human actions in consumer photographs. We construct a new dataset available at http: www.di.ens.fr willow research stillactions with seven classes of actions in 911 Flickr images representing natural variations of human actions in terms of camera view-point, human pose, clothing, occlusions and scene background. We study action recognition in still images using the state-of-the-art bag-of-features methods as well as their combination with the part-based Latent SVM approach of In particular, we investigate the role of background scene context and demonstrate that improved action recognition performance can be achieved by (i) combining the statistical and part-based representations, and (ii) integrating person-centric description with the background scene context. We show results on our newly collected dataset of seven common actions as well as demonstrate improved performance over existing methods on the datasets of and Yao and Fei-Fei. A graph-based method for 3D view-invariant human action recognition is proposed.An action is represented as a sequence of graphs.The vertices can either be tracked skeleton joints or spatio-temporal keypoints.A spectral graph wavelet transform is leveraged to extract features from the graphs.The method is useful for both single- and multi-view action recognition tasks. Display Omitted We present a method for view-invariant action recognition from depth cameras based on graph signal processing techniques. Our framework leverages a novel graph representation of an action as a temporal sequence of graphs, onto which we apply a spectral graph wavelet transform for creating our feature descriptor. We evaluate two view-invariant graph types: skeleton-based and keypoint-based. The skeleton-based descriptor captures the spatial pose of the subject, whereas the keypoint-based is able to capture complementary information about human-object interaction and the shape of the point cloud. We investigate the effectiveness of our method by experiments on five publicly available datasets. By the graph structure, our method captures the temporal interaction between depth map interest points and achieves a 19.8 increase in performance compared to state-of-the-art results for cross-view action recognition, and competing results for frontal-view action recognition and human-object interaction. Namely, our method results in 90.8 accuracy on the cross-view N-UCLA Multiview Action3D dataset and 91.4 accuracy on the challenging MSRAction3D dataset in the cross-subject setting. For human-object interaction, our method achieves 72.3 accuracy on the Online RGBD Action dataset. We also achieve 96.0 and 98.8 accuracy on the MSRActionPairs3D and UCF-Kinect datasets, respectively. We consider the problem of recognizing human actions from still images. We propose a novel approach that treats the pose of the person in the image as latent variables that will help with recognition. Different from other work that learns separate systems for pose estimation and action recognition, then combines them in an ad-hoc fashion, our system is trained in an integrated fashion that jointly considers poses and actions. Our learning objective is designed to directly exploit the pose information for action recognition. Our experimental results demonstrate that by inferring the latent poses, we can improve the final action recognition results.