text: Deep convolutional networks have achieved great success for image recognition. However, for action recognition in videos, their advantage over traditional methods is not so evident. We present a general and flexible video-level framework for learning action models in videos. This method, called temporal segment network (TSN), aims to model long-range temporal structures with a new segment-based sampling and aggregation module. This unique design enables our TSN to efficiently learn action models by using the whole action videos. The learned models could be easily adapted for action recognition in both trimmed and untrimmed videos with simple average pooling and multi-scale temporal window integration, respectively. We also study a series of good practices for the instantiation of TSN framework given limited training samples. Our approach obtains the state-the-of-art performance on four challenging action recognition benchmarks: HMDB51 (71.0 ), UCF101 (94.9 ), THUMOS14 (80.1 ), and ActivityNet v1.2 (89.6 ). Using the proposed RGB difference for motion models, our method can still achieve competitive accuracy on UCF101 (91.0 ) while running at 340 FPS. Furthermore, based on the temporal segment networks, we won the video classification track at the ActivityNet challenge 2016 among 24 teams, which demonstrates the effectiveness of TSN and the proposed good practices.
  @cite_67: With the improved accessibility to an exploding amount of video data and growing demands in a wide range of video analysis applications, video-based action recognition classification becomes an increasingly important task in computer vision. In this paper, we propose a two-layer structure for action recognition to automatically exploit a mid-level acton'' representation. The actons are learned via a new max-margin multi-channel multiple instance learning framework. The learned actons (with no requirement for detailed manual annotations) thus observe a property of being compact, informative, discriminative, and easy to scale. This is different from the standard unsupervised (e.g. k-means) or supervised (e.g. random forests) coding strategies in action recognition. Applying the learned actons in our two-layer structure yields the state-of-the-art classification performance on Youtube and HMDB51 datasets.
  @cite_37: We address the classic problems of detection, segmentation and pose estimation of people in images with a novel definition of a part, a poselet. We postulate two criteria (1) It should be easy to find a poselet given an input image (2) it should be easy to localize the 3D configuration of the person conditioned on the detection of a poselet. To permit this we have built a new dataset, H3D, of annotations of humans in 2D photographs with 3D joint information, inferred using anthropometric constraints. This enables us to implement a data-driven search procedure for finding poselets that are tightly clustered in both 3D joint configuration space as well as 2D image appearance. The algorithm discovers poselets that correspond to frontal and profile faces, pedestrians, head and shoulder views, among others. Each poselet provides examples for training a linear SVM classifier which can then be run over the image in a multiscale scanning mode. The outputs of these poselet detectors can be thought of as an intermediate layer of nodes, on top of which one can run a second layer of classification or regression. We show how this permits detection and localization of torsos or keypoints such as left shoulder, nose, etc. Experimental results show that we obtain state of the art performance on people detection in the PASCAL VOC 2007 challenge, among other datasets. We are making publicly available both the H3D dataset as well as the poselet parameters for use by other researchers.
  @cite_15: This paper proposes a multi-level video representation by stacking the activations of motion features, atoms, and phrases (MoFAP). Motion features refer to those low-level local descriptors, while motion atoms and phrases can be viewed as mid-level "temporal parts". Motion atom is defined as an atomic part of action, and captures the motion information of video in a short temporal scale. Motion phrase is a temporal composite of multiple motion atoms defined with an AND OR structure. It further enhances the discriminative capacity of motion atoms by incorporating temporal structure in a longer temporal scale. Specifically, we first design a discriminative clustering method to automatically discover a set of representative motion atoms. Then, we mine effective motion phrases with high discriminative and representative capacity in a bottom-up manner. Based on these basic units of motion features, atoms, and phrases, we construct a MoFAP network by stacking them layer by layer. This MoFAP network enables us to extract the effective representation of video data from different levels and scales. The separate representations from motion features, motion atoms, and motion phrases are concatenated as a whole one, called Activation of MoFAP. The effectiveness of this representation is demonstrated on four challenging datasets: Olympic Sports, UCF50, HMDB51, and UCF101. Experimental results show that our representation achieves the state-of-the-art performance on these datasets.
  @cite_29: We describe a mid-level approach for action recognition. From an input video, we extract salient spatio-temporal structures by forming clusters of trajectories that serve as candidates for the parts of an action. The assembly of these clusters into an action class is governed by a graphical model that incorporates appearance and motion constraints for the individual parts and pairwise constraints for the spatio-temporal dependencies among them. During training, we estimate the model parameters discriminatively. During classification, we efficiently match the model to a video using discrete optimization. We validate the model's classification ability in standard benchmark datasets and illustrate its potential to support a fine-grained analysis that not only gives a label to a video, but also identifies and localizes its constituent parts.
  @cite_19: Activity recognition in video is dominated by low- and mid-level features, and while demonstrably capable, by nature, these features carry little semantic meaning. Inspired by the recent object bank approach to image representation, we present Action Bank, a new high-level representation of video. Action bank is comprised of many individual action detectors sampled broadly in semantic space as well as viewpoint space. Our representation is constructed to be semantically rich and even when paired with simple linear SVM classifiers is capable of highly discriminative performance. We have tested action bank on four major activity recognition benchmarks. In all cases, our performance is better than the state of the art, namely 98.2 on KTH (better by 3.3 ), 95.0 on UCF Sports (better by 3.7 ), 57.9 on UCF50 (baseline is 47.9 ), and 26.9 on HMDB51 (baseline is 23.2 ). Furthermore, when we analyze the classifiers, we find strong transfer of semantics from the constituent action detectors to the bank classifier.
  @cite_31: This paper presents a novel approach for analyzing human actions in non-scripted, unconstrained video settings based on volumetric, x-y-t, patch classifiers, termed actemes. Unlike previous action-related work, the discovery of patch classifiers is posed as a strongly-supervised process. Specifically, key point labels (e.g., position) across space time are used in a data-driven training process to discover patches that are highly clustered in the space time key point configuration space. To support this process, a new human action dataset consisting of challenging consumer videos is introduced, where notably the action label, the 2D position of a set of key points and their visibilities are provided for each video frame. On a novel input video, each acteme is used in a sliding volume scheme to yield a set of sparse, non-overlapping detections. These detections provide the intermediate substrate for segmenting out the action. For action classification, the proposed representation shows significant improvement over state-of-the-art low-level features, while providing spatiotemporal localization as additional output, which sheds further light into detailed action understanding.
  @cite_10: How should a video be represented? We propose a new representation for videos based on mid-level discriminative spatio-temporal patches. These spatio-temporal patches might correspond to a primitive human action, a semantic object, or perhaps a random but informative spatio-temporal patch in the video. What defines these spatio-temporal patches is their discriminative and representative properties. We automatically mine these patches from hundreds of training videos and experimentally demonstrate that these patches establish correspondence across videos and align the videos for label transfer techniques. Furthermore, these patches can be used as a discriminative vocabulary for action classification where they demonstrate state-of-the-art performance on UCF50 and Olympics datasets.
  @cite_25: This paper proposes motionlet, a mid-level and spatiotemporal part, for human motion recognition. Motion let can be seen as a tight cluster in motion and appearance space, corresponding to the moving process of different body parts. We postulate three key properties of motion let for action recognition: high motion saliency, multiple scale representation, and representative-discriminative ability. Towards this goal, we develop a data-driven approach to learn motion lets from training videos. First, we extract 3D regions with high motion saliency. Then we cluster these regions and preserve the centers as candidate templates for motion let. Finally, we examine the representative and discriminative power of the candidates, and introduce a greedy method to select effective candidates. With motion lets, we present a mid-level representation for video, called motionlet activation vector. We conduct experiments on three datasets, KTH, HMDB51, and UCF50. The results show that the proposed methods significantly outperform state-of-the-art methods.
summary: To overcome the limitation of local descriptors, several mid-level representations have been proposed for action recognition @cite_29 @cite_10 @cite_25 @cite_31 @cite_67 @cite_15 @cite_19 . Raptis @cite_29 grouped similar trajectories into clusters, each of which was regarded as an action part. Jain @cite_10 extended the idea of discriminative patches into videos and proposed discriminative spatio-temporal patches for representing videos. Zhang @cite_31 proposed to discover a set of mid-level patches in a strongly-supervised manner. Similar to 2-D poselet @cite_37 , they tightly clustered action parts using human joint labeling, dubbed . Wang @cite_25 proposed a data-driven approach to discover those effective parts with high motion salience, known as motionlet . Zhu @cite_67 proposed a two-layer representation for action recognition. The weakly-supervised actons were learned via a max-margin multi-channel multiple instance learning framework. Wang @cite_15 proposed a multiple level representation called as MoFAP by concatenating motion features, atoms, and phrases. Sadanand @cite_19 presented a high-level video representation called as Action Bank by using a set action templates to describe the video content. In summary, these mid-level representations have the merits of representative and discriminative power, but still depends on the low-level hand-crafted features.