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: Comparative survey: People detection, tracking and multi-sensor Fusion in a video sequence 
Chapter Title:  DATA FUSION 
1. Abstract of Cited Paper (BIB001):  This paper shows how the output of a number of detection and tracking algorithms can be fused to achieve robust tracking of people in an indoor environment. The new tracking system contains three co-operating parts: i) an Active Shape Tracker using a PCA-generated model of pedestrian outline shapes, ii) a Region Tracker, featuring region splitting and merging for multiple hypothesis matching, and iii) a Head Detector to aid in the initialisation of tracks. Data from the three parts are fused together to select the best tracking hypotheses.The new method is validated using sequences from surveillance cameras in a underground station. It is demonstrated that robust realtime tracking of people can be achieved with the new tracking system using standard PC hardware. 
2. Abstract of Cited Paper (BIB002):  This article presents four different approaches for identifying persons during tracking. The ability to keep track of people is essential for mobile robots which provide services to humans. The tracking task is usually carried out using range measuring devices like laser range sensors, which provide spatial information only. They allow to estimate the trajectories of objects quite accurately, but they do not allow to directly distinguish between the objects being observed. The tracking algorithms, therefore, tend to confuse tracks in difficult situations, like persons walking in close proximity to each other. The techniques presented in this article mitigate this problem by fusing the spatial measurements with different types of complimentary sensor data. The first technique uses colour information extracted from camera images to distinguish between persons, based on the colour of the clothes they wear. The second technique uses reflectance intensities, which can directly be provided by the laser range sensors for this purpose. The third method is again based on camera information, but it employs a probabilistic shape and motion model for each person it tracks, in order to distinguish between them. Finally, we present an approach that uses a network of dedicated sensors, which directly transmit identification information for each person. The aim of this paper is to give a comparative overview of these four methods, and to discuss their drawbacks and advantages. 
3. Abstract of Cited Paper (BIB003):  There has been an ever-increasing interest in multi-disciplinary research on multisensor data fusion technology, driven by its versatility and diverse areas of application. Therefore, there seems to be a real need for an analytical review of recent developments in the data fusion domain. This paper proposes a comprehensive review of the data fusion state of the art, exploring its conceptualizations, benefits, and challenging aspects, as well as existing methodologies. In addition, several future directions of research in the data fusion community are highlighted and described.

Literature Review:
Data fusion BIB003 BIB004 is the process of combining information from a number of different sources to improve the certainty and accuracy of the information. Tracking of moving people in a video can be improved by fusing tracking of different camera capturing the same person from different angles BIB002 , BIB001 as shown Fig. 8 . Two techniques of multi-sensor data fusion called weighted sum and Winner-take-all are compared in this paper. The first fusing method works by taking a weighted sum of the filtered points. The second fusion method proposed works based on the rule winner-takeall. It considers the filtered point with high value as the correct answer.