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: Class Energy Image Analysis for Video Sensor-Based Gait Recognition: A Review 
Chapter Title:  Gait Information Fusion Approach 
1. Abstract of Cited Paper (BIB001):  In this paper, we propose a gait recognition algorithm that fuses motion and static spatio-temporal templates of sequences of silhouette images, the Motion Silhouette Contour Templates (MSCTs) and Static Silhouette Templates (SSTs). The performance of the proposed algorithm is evaluated experimentally using the SOTON dataset and the USF dataset. The proposed algorithm has a recognition rate of around 80% in intrinsic difference group (Probe A-C) of USF dataset which is 14% higher than the baseline algorithm. 
2. Abstract of Cited Paper (BIB002):  Shape and kinematics are two important cues in human movement analysis. Due to real difficulties in extracting kinematics from videos accurately, this paper proposes to address the problem of human action recognition by spatiotemporal shape analysis. Without explicit feature tracking and complex probabilistic modeling of human movements, we directly convert an associated sequence of human silhouettes derived from videos into two types of computationally efficient representations, i.e., average motion energy and mean motion shape, to characterize actions. Supervised pattern classification techniques using various distance measures are used for recognition. The encouraging experimental results are obtained on a recent dataset including 10 different actions from 9 subjects. 
3. Abstract of Cited Paper (BIB003):  In this paper, we propose a novel algorithm for gait recognition. Binarized silhouette of a motion object is first segmented from color image, and then, spatio-temporal (XYT) volume is constructed by using these binarized silhouettes, and cut at knee and hip height. Next, energy images are extracted by projecting these three individual XYT volumes onto X-T plane, respectively. Fourier transform is employed as a processing step to achieve translation invariant for the silhouette sequences which are captured from the subjects walk in different speed. Then three frequency-domain feature vectors are fused. AdaBoost is used to select a small set of critical features from all of the features. Nearest neighbor and support vector machine (SVM) classifier are finally executed to produce final decision, respectively. The experiments are carried on one of the largest public gait database: the CASIA database. The experimental results show that the proposed algorithm is efficient for human gait recognition, and achieves competitive performance. 
4. Abstract of Cited Paper (BIB004):  In this paper, we propose a gait recognition algorithm that fuses motion and static spatio-temporal templates of sequences of silhouette images, the motion silhouette contour templates (MSCTs) and static silhouette templates (SSTs). MSCTs and SSTs capture the motion and static characteristic of gait. These templates would be computed from the silhouette sequence directly. The performance of the proposed algorithm is evaluated experimentally using the SOTON data set and the USF data set. We compared our proposed algorithm with other research works on these two data sets. Experimental results show that the proposed templates are efficient for human identification in indoor and outdoor environments. The proposed algorithm has a recognition rate of around 85% on the SOTON data set. The recognition rate is around 80% in intrinsic difference group (probes A-C) of USF data set.

Literature Review:
Gait information fusion approach employs feature layer fusion and decision layer fusion method BIB001 to achieve the fusion of static, dynamic and temporal information. Gait information fusion method usually includes X-T Plane Energy Image (X-T PEI) BIB004 , Color Gait History Image (CGHI)  , Motion Silhouette Contour Template (MSCT), Static Silhouette Template (SST) BIB002 BIB005 , Mean Motion Shape (MMS) and Average Motion Energy (AME) BIB003 , etc. Guo et al. BIB004 reformulated a silhouette sequence as a third-order tensor with column, row and time modes, which was XYT form. The human body can be divided into three parts when the hip and knee are viewed as the demarcation point, and the body is mapped to the X-T plane. X-T Plane Energy Image (X-T PEI) can be generated by Equation (17).  where H is the body height of the silhouette images. The three X-T PEIs, which has static and dynamic information, are fused based on series in the feature layer. The X-T PEIs of the three body parts are represented in Figure 10a -c. Moreover, X-T PEI can detect the gait period. Further improved SHI  , Chen  devised the Color Gait History Image (CGHI) to describe the temporal-spatial gait information. CGHI consists of three channels. The channels of R and G are fSHIs which views the standing on one leg and two legs as the start of a period, respectively. Moreover, the channel of B is GEI. Equation (17) if A x y n E x y n otherwise A x y n P A x y n B x y n B x y n if E x y n E x y n E x y n otherwise where P is the number of frames in a gait period. ( ( , , )) is the eroded silhouettes. S is the structuring element. MSCT contains information about the movement characteristics of a human gait and SST embeds information about the static characteristics of a human gait. These templates are used together for gait recognition. Wang et al. BIB003 adopted two templates, which were Mean Motion Shape (MMS) and Average Motion Energy (AME), to describe the overall shape and the moving parts' features. The two templates can be calculated as follows: AME is the same as GEI, which is shown in Figure 10n . MMS describes the changes of the shape contour, which is attained by utilizing the edge tracking algorithm. The normalized contour can be represented as 1 2 [ , , , ] MMS is the eigenvector that the largest eigenvalue of MMS E corresponds to. The example of MMS is demonstrated in Figure 10o . AME and MMS are used for recognizing respectively. The recognition results are fused together on decision layer. Table 3 characterizes the expressions, motion information and computational complexity of the gait information fusion approach. Class Energy Images of gait information fusion approach have inconsistent computational complexity. In addition, gait information fusion approach mainly takes different feature images into consideration, and there is little correlation between different feature images. It is a promising direction to study the extraction of different features, and to fuse them using different fusion strategy. Table 3 . The information of the Gait information fusion approach.