You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Survey on Video Transmission Techniques 
Chapter Title:  Intelligent Behavioral Analysis 
1. Abstract of Cited Paper (BIB001):  Motion analysis in broadcast sports video is a challenging problem especially for player action recognition due to the low resolution of players in the frames. This paper presents a novel approach to recognize the basic player actions in broadcast tennis video where the player is about 30 pixels tall. Two research challenges, motion representation and action recognition, are addressed. A new motion descriptor, which is a group of histograms based on optical flow, is proposed for motion representation. The optical flow here is treated as spatial pattern of noisy measurement instead of precise pixel displacement. To recognize the action performed by the player, support vector machine is employed to train the classifier where the concatenation of histograms is formed as the input features. The experimental results demonstrate that our method is promising. 
2. Abstract of Cited Paper (BIB002):  One of the key challenges in human action recognition from video sequences is how to model an action sufficiently. Therefore, in this paper we propose a novel motion-based representation called Motion Context (MC), which is insensitive to the scale and direction of an action, by employing image representation techniques. A MC captures the distribution of the motion words (MWs) over relative locations in a local region of the motion image (MI) around a reference point and thus summarizes the local motion information in a rich 3D MC descriptor. In this way, any human action can be represented as a 3D descriptor by summing up all the MC descriptors of this action. For action recognition, we propose 4 different recognition configurations: MW+pLSA, MW+SVM, MC+w 3-pLSA (a new direct graphical model by extending pLSA), and MC+SVM. We test our approach on two human action video datasets from KTH and Weizmann Institute of Science (WIS) and our performances are quite promising. For the KTH dataset, the proposed MC representation achieves the highest performance using the proposed w 3-pLSA. For the WIS dataset, the best performance of the proposed MC is comparable to the state of the art. 
3. Abstract of Cited Paper (BIB003):  Vision-based human action recognition is the process of labeling image sequences with action labels. Robust solutions to this problem have applications in domains such as visual surveillance, video retrieval and human-computer interaction. The task is challenging due to variations in motion performance, recording settings and inter-personal differences. In this survey, we explicitly address these challenges. We provide a detailed overview of current advances in the field. Image representations and the subsequent classification process are discussed separately to focus on the novelties of recent research. Moreover, we discuss limitations of the state of the art and outline promising directions of research.

Literature Review:
Human activity analysis is receiving increasing attention from computer vision researchers. This interest is motivated by a wide spectrum of applications, such as surveillance, man-machine interfaces, video coding, and so on. It has been a hot research topic in image analysis, psychology, and neurophysiology. Compared with object identification and the tracking technique, action recognition technology is at a higher research level. At present, although some action details in video intelligent analysis system cannot be recognized accurately, analysis of objects' overall behaviors have been achieved, such as track, shape, color, running speed, velocity change and volume of objects. By analyzing these data, some demands for features in the field of video application, such as alarm about restricted zone, quantitative statistics, medical monitoring and intelligent conference, can be realized. Intelligent behavioral analysis involves extraction and expression of motion features at the bottom layer, representing methods of behaviors and inference patterns of behavioral semantics on the higher layer. Image representations can be divided into two categories BIB004 : global representations and local representations. Local representations describe the observation as a collection of independent patches. The calculation of local representations proceeds in a bottom-up fashion: first, we will detect spatiotemporal interest points and calculated local patches around these points, then we combine the patches into a final representation. Global representations encode the Region Of Interest (ROI) of a person as a whole. The ROI is usually obtained through background subtraction or tracking. Common global representations are derived from silhouettes, edges or optical flow. Extraction and expression of vision or other motion features at the bottom layer are basic work needed by implementation of inference about behavioral comprehension on the higher layer. Common ways to express moving targets include static characteristic based on edge, dynamic characteristic based on light stream and spatiotemporal feature obtained from some parts. The method based on spatiotemporal interest point can provide abundant description and expression. In this method BIB003 , spatiotemporal words are composed of extracted spatiotemporal interest points for each segment of video sequence. In complicated videos, they can be expressed by response function of linear filter. (9) Where I stands for image; g(x,y, ) is two-dimensional Gaussian smoothing kernel, which is appropriate for spatial dimension (x, y); hev and hod are one-dimensional orthometric filter applied to time dimension. When action recognition is carried out, we usually match recognized image with behavioral reference sequence set is selected in advanced in order to judge specific behaviors. It is a typical classification problem. Action recognition can be realized by two methods, i.e., state-space method and template matching BIB001 . The state-space model defines each static posture as a stage, i.e., these states are connected by a certain probability. When the state-space model carries out action recognition, it implements traversal for all of these different states and then chooses maximum joint probability as the result of recognition. The most representative statespace model is Hidden Markov Model (HMM) BIB002 . HMM can process time-varying data effectively and has learning and recognition capacity. Template matching coverts representative images into a group of static templates in advance and then compare images with behavioral templates in the process of recognition. In addition, dynamic bayesian network (DBN), conditional random field (CRF) and decisionmaking tree can be applied to recognition. Currently, researches on behavioral analysis usually only involve action recognition. Processed behaviors can be divided into two types. One type contains finite rule behaviors which are quite simple, such as sitting, walking, running, standing and crouching. The other type refers to finite behaviors processed in a specific scene, such as driving fatigue recognition and fighting. In this scene, behaviors have strict limitations and behavior description usually adopts speed or trail of movement. However, there is a huge gap between comprehension of behavioral analysis in a random scene and practical application.