# Human Behavior Analysis: A Survey on Action Recognition
## 2 Human Action Recognition
### Global Representations  
Taking into account the holistic view of the scene, which may include different actions simultaneously, the image representation is described as a global representation. By capturing the motion information of the entire human subject, global representations are richer and express better and more concise motion information. Although they are susceptible to noise, the current advances in human detector [39; 40; 41; 42], human tracker [43; 44; 45; 46], and multi-person tracker [47; 48; 49; 50] algorithms, make it easier to achieve high accuracy even with occlusions, different viewpoints, or noise, as shown in Figure 4. Despite the object detectors and trackers accuracy, they capture the information in a certain rectangle region, which may introduce some noise and irrelevant information, not only from the human appearance but also the cluttered background. Therefore, in order to take advantage of those powerful algorithms, usually, some earlier feature extraction is required, rather than using a raw input of person's localisation for the extraction of human dynamics. Following a region proposal network (RPN), [PERSON] et al. [51] proposed a spatial RPN analysing one frame and a motion RPN analysing the optical flow of its neighbouring frames (flow of 5 frames). Their architecture was based on faster R-CNN [52] for region proposals, and all the regions from both streams are fused before the ROI pooling layer. Resorting to the single-shot multibox detector (SSD) framework [40], [PERSON] et al. [53] extend the anchor boxes to anchor cuboids over subsequent frames, extracting the 2D convolutional features with shared weights between frames. Engaging 3D ConvNets, [PERSON] et al. [54] extract motion information through the analysis of two-streams, RGB frames and optical flow of the clip with an Inception 3D ConvNet. They employed faster R-CNN [52] for region proposals, applying ROI pooling on both branches of their network, and average pooling is used at the feature map level to fuse them. Recognising human dynamics as a regression problem, [PERSON] et al. [55] employed a 3D ResNext-101 [56] to extract temporal information from a clip video and use a 2D-CNN branch on the most recent frame of the clip to address the spatial localisation, stacking both resulting features from the networks and following the same guidelines as YOLOv2 [57] for the bounding box regression. Employing a progressive learning framework, [PERSON] et al. [58], in order to refine the cuboid proposals towards spatiotemporal action localisation, proposed a multi-step optimisation process to refine initial proposals progressively. They used a two-stream architecture for spatial refinement and temporal extent, where the spatial branch performs bounding box regression at each frame, taking into account the temporal extent in order to update the proposals regarding the cuboids extension through a 3D ConvNet. Moreover, also analysing cuboids, [PERSON] et al. [59] presented an action tubelet (cuboid) detector, denoted as a moving centre detector. Treating an action tubelet instance as a trajectory of moving points, they employed a three-branch framework, where the centre branch detects the action instance centre and classification. The movement branch estimates the offset estimation in the current frame concerning its centre, and finally, the box branch predicts the bounding box size over the predicted centre point. [PERSON] et al. [60] exploited both spatial and temporal information through different frame rates over a two-stream architecture. A fast and a slow pathway, where the fast one (high frame rate) will extract  
Figure 4: Multi-person tracking example employing the FAIRMOT tracker [50]. temporal information through a 3D ConvNet, and the slow one (at low frame rate) will analyse only spatial information taking into account the temporal dynamics. Its slow pathway is able to localise an action based on the fast pathway.