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 Review on Human Actions Recognition Using Vision Based Techniques 
Chapter Title:  I. INTRODUCTION 
1. Abstract of Cited Paper (BIB001):  W/sup 4/ is a real time visual surveillance system for detecting and tracking multiple people and monitoring their activities in an outdoor environment. It operates on monocular gray-scale video imagery, or on video imagery from an infrared camera. W/sup 4/ employs a combination of shape analysis and tracking to locate people and their parts (head, hands, feet, torso) and to create models of people's appearance so that they can be tracked through interactions such as occlusions. It can determine whether a foreground region contains multiple people and can segment the region into its constituent people and track them. W/sup 4/ can also determine whether people are carrying objects, and can segment objects from their silhouettes, and construct appearance models for them so they can be identified in subsequent frames. W/sup 4/ can recognize events between people and objects, such as depositing an object, exchanging bags, or removing an object. It runs at 25 Hz for 320/spl times/240 resolution images on a 400 MHz dual-Pentium II PC. 
2. Abstract of Cited Paper (BIB002):  This paper deals with real-time image processing of crowded outdoor scenes with the objective of creating an effective traffic management system that monitors urban settings (urban intersections, streets after athletic events, etc.). The proposed system can detect, track, and monitor both pedestrians (crowds) and vehicles. We describe the characteristics of the tracker that is based on a new detection method. Initially, we produce a motion estimation map. This map is then segmented and analyzed in order to remove inherent noise and focus on particular regions. Moreover, tracking of these regions is obtained in two steps: fusion and measurement of the current position and velocity, and then estimation of the next position based on a simple model. The instability of tracking is addressed by a multiple-level approach to the problem. The computed data are then analyzed to produce motion statistics. Experimental results from various sites in the Twin Cities area are presented. The final step is to provide this information to an urban traffic management center that monitors crowds and vehicles in the streets. 
3. Abstract of Cited Paper (BIB003):  This paper proposes a solution for the automatic detection and tracking of human motion in image sequences. Due to the complexity of the human body and its motion, automatic detection of 3D human motion remains an open, and important, problem. Existing approaches for automatic detection and tracking focus on 2D cues and typically exploit object appearance (color distribution, shape) or knowledge of a static background. In contrast, we exploit 2D optical flow information which provides rich descriptive cues, while being independent of object and background appearance. To represent the optical flow patterns of people from arbitrary viewpoints, we develop a novel representation of human motion using low-dimensional spatio-temporal models that are learned using motion capture data of human subjects. In addition to human motion (the foreground) we probabilistically model the motion of generic scenes (the background); these statistical models are defined as Gibbsian fields specified from the first-order derivatives of motion observations. Detection and tracking are posed in a principled Bayesian framework which involves the computation of a posterior probability distribution over the model parameters (i.e., the location and the type of the human motion) given a sequence of optical flow observations. Particle filtering is used to represent and predict this non-Gaussian posterior distribution over time. The model parameters of samples from this distribution are related to the pose parameters of a 3D articulated model (e.g. the approximate joint angles and movement direction). Thus the approach proves suitable for initializing more complex probabilistic models of human motion. As shown by experiments on real image sequences, our method is able to detect and track people under different viewpoints with complex backgrounds. 
4. Abstract of Cited Paper (BIB004):  A series of mono, bi and tricarbocyclic compounds, most of which have olefinic unsaturation in the ring, which may or may not have substituents thereon. While the bi and tricyclic rings may be unsubstituted, these compounds which have olefinic unsaturation, particularly multiple olefinic unsaturation, are polymerizable and copolymerizable in known polymerization systems. They are particularly good crosslinking agents. These compounds are further useful in the sense that they can be cleaved oxidatively, to corresponding carboxylic acids, aldehydes and/or alcohols which have known utility in the plasticizer and detergent arts. The compounds which do not have olefinic unsaturation can also be oxidatively cleaved to produce oxygenated, e.g., acid, alcohol or aldehyde, compounds having known utility. 
5. Abstract of Cited Paper (BIB005):  With the proliferation of security cameras, the approach taken to monitoring and placement of these cameras is critical. This paper presents original work in the area of multiple camera human activity monitoring. First, a system is presented that tracks pedestrians across a scene of interest and recognizes a set of human activities. Next, a framework is developed for the placement of multiple cameras to observe a scene. This framework was originally used in a limited X, Y, pan formulation but is extended to include height (Z) and tilt. Finally, an active dual-camera system for task recognition at multiple resolutions is developed and tested. All of these systems are tested under real-world conditions, and are shown to produce usable results. 
6. Abstract of Cited Paper (BIB006):  The past decade has witnessed a rapid proliferation of video cameras in all walks of life and has resulted in a tremendous explosion of video content. Several applications such as content-based video annotation and retrieval, highlight extraction and video summarization require recognition of the activities occurring in the video. The analysis of human activities in videos is an area with increasingly important consequences from security and surveillance to entertainment and personal archiving. Several challenges at various levels of processing-robustness against errors in low-level processing, view and rate-invariant representations at midlevel processing and semantic representation of human activities at higher level processing-make this problem hard to solve. In this review paper, we present a comprehensive survey of efforts in the past couple of decades to address the problems of representation, recognition, and learning of human activities from video and related applications. We discuss the problem at two major levels of complexity: 1) "actions" and 2) "activities." "Actions" are characterized by simple motion patterns typically executed by a single human. "Activities" are more complex and involve coordinated actions among a small number of humans. We will discuss several approaches and classify them according to their ability to handle varying degrees of complexity as interpreted above. We begin with a discussion of approaches to model the simplest of action classes known as atomic or primitive actions that do not require sophisticated dynamical modeling. Then, methods to model actions with more complex dynamics are discussed. The discussion then leads naturally to methods for higher level representation of complex activities.

Literature Review:
Recognizing human actions have been very challenging for computer vision scientists and researcher since the last two decades. Human Action Recognition systems have a wide spread applications in surveillance, pedestrian tracking and Human Machine Interaction (HMI) BIB001 . These systems often employ techniques such as robust foreground segmentation, people tracking and occlusion handling. But event then these technologies are not matured enough to be fully deployed somewhere. The vision-based action recognition systems entail low resolution task and high resolution tasks BIB006 . The low resolution tasks incorporate human body as a whole e.g. center of mass, area, volume and velocity of entire body BIB005 , BIB003 . On the other hand, high resolution tasks manifests measurement and relation of individual body parts e.g. human pose, silhouette, position and velocity of hands, feet etc.  , BIB004 , BIB002 . There exist two types of shape-based features 1) silhouette 2) contour. The first technique performs on all pixel areas in the polygon  . While second technique works well for outer boundary of objects in an image. Contour-based methods work on Fourier Transform, Artificial Neural Network and Hough Transform. The activity recognition has also been carried out by researchers using micro sensor-based systems. The main reason for deployment of such system is that they are low-power, cost-effective and privacy-aware. The Manuscript received January 6, 2013; revised May 12, 2014. Activity based recognition system should have the following key features: 1. Robustness: Accurate extraction of features from sequences 2. Discriminative: it should be discriminative instead of generative model 3. Rejection: Activity recognition should not be exhaustive for foreseeable future. The activity-based recognition systems work in a hierarchical fashion. At primary level, object detection and tracking is done. At mid-level, atomic actions are recognized using various classification techniques. At high level, these atomic actions are linked together to figure out an activity BIB007 . The goal is to recognize the activities from a series of image frames. In a simple scenario, a set of images contains a human action, the target is to recognize the action correctly. In general scenarios the continuous actions of humans are recognized with start and end time of the action in the set of frames of image. In this review paper a number of approaches have been discussed to recognize human actions and classify them. In this discussion, we refer the simple motion patterns performed by single person and duration lasted for short intervals of time are Actions. Some examples of different actions are; bending, walking, running etc. A hierarchical approach is used for the human actions recognition system, where the lower levels are on segmentation, detection and tracking, middle level on action recognition and higher level are the classifiers. Human actions are categorized into four different categories such as: gestures, poses, interactions and activities. Gestures are the basic and atomic motions of a body part such as waving a hand etc. Poses are not atomic motions of a human body parts instead it is comprised of a multiple motions of a human body parts of a single person such as walking etc. Interaction is consists of more than one person actions such as fighting etc. Activities are performed by a group of people trying to perform actions together like meeting etc. The main goal of this paper is to give a review on various methodologies to recognize human actions. We talk about various techniques made to recognize various levels of actions performed by humans. A survey performed by Aggarwal and Cai in 1999 BIB001 has achieved the goal to recognize lower level actions such as: tracking and body posture analysis. However this analysis was not sufficient enough and we have to describe the higher level actions. So we conduct this survey to perform higher level actions to be recognized. The vision based recognition becomes the primary goal to recognize the actions automatically. After recognizing and classifying the actions of human, we can identify the abnormal and suspicious actions from normal actions and this will be helpful in predicting the threats and terrorist attacks. As shown in Fig. 1 , the lower level aims to find the regions of interest from the image sequences while the higher level focuses on the recognition of temporal motion patterns. Our primary focus in this paper is to briefly explain the existing techniques used to recognize human actions in vision based technology.