Comparative survey: People detection, tracking and multi-sensor Fusion in a video sequence <s> TRACKING <s> In this paper we have developed a Matlab/Simulink based model for monitoring a contact in a video surveillance sequence. For the segmentation process and corect identification of a contact in a surveillance video, we have used the Horn-Schunk optical flow algorithm. The position and the behavior of the correctly detected contact were monitored with the help of the traditional Kalman filter. After that we have compared the results obtained from the optical flow method with the ones obtained from the Kalman filter, and we show the correct functionality of the Kalman filter based tracking. The tests were performed using video data taken with the help of a fix camera. The tested algorithm has shown promising results. Index Terms — Video Surveillance System, Optical Flow, Kalman Filtering, Image Processing, Tracking <s> BIB001 </s> Comparative survey: People detection, tracking and multi-sensor Fusion in a video sequence <s> TRACKING <s> Both detection and tracking people are challenging problems, because the human body is non-rigid and there is occasion between the body block. In general, human detection is a prerequisite for human tracking, and tracking has no effect on human detection. However, a novel approach is proposed for human detection and tracking in this paper, changing this situation. First, improved HOG is used to extract human features in the image. Second, we make the relationship between human detection and tracking closer-detection is not only the prerequisite of tracking and it also benefits from tracking. Finally, the Kalman filter is introduced into detecting and tracking people. Our experiments have demonstrated that such a method reduces detection time, and improves human detection and tracking accuracy. <s> BIB002 </s> Comparative survey: People detection, tracking and multi-sensor Fusion in a video sequence <s> TRACKING <s> The moving object detection is a prerequisite and difficult point to realize tracking in the video tracking system. In order to detect moving object effectively, an object tracking algorithm is proposed based on combination of dynamic template matching and Kalman filter. First, get the area of the moving object by using inter-frame difference method and extract the SIFT feature points. Then, find the location of the candidate object that is most matched with the object template in the search area by Kalman filter and match it with the object template in the current frame. Finally, the feature points' loss rate will serve as the limited threshold, and we update template according to dynamic template updating strategy. By the number of the frames of the target's matching failures we determine whether the moving target is disappeared. Several experiments of the object tracking show that the approach is accurate and fast, and it has a better robust performance during the attitude changing, the size changing and the shelter instance. <s> BIB003 </s> Comparative survey: People detection, tracking and multi-sensor Fusion in a video sequence <s> TRACKING <s> Tracking object in video sequence is receiving enormous interest in computer vision research. This paper we contrast performance of Mean-Shift algorithm's gradient descent based search strategy with Kalman Filter based tracking algorithm used to models the dynamic motion of target object to guide optimize object's position through time using Swarm Intelligence based Particle Swarm Optimization. Experimental results of tracking a car demonstrate that the proposed Kalman Filter for object tracking is efficient under dynamic environment, robust in occlusion comes at the cost of higher computational requirement, helps to separate object pixel from backgroundpixel for fast moving object. And optimize time for all vehicles detected in video are calculated by Particle Swarm Optimization. <s> BIB004
Tracking is the process of finding trajectories of objects over time from a given sequence of images. There are two approaches of tracking methods. The first is deterministic methods where tracking is performed by an iterative search for a local maximum of similarity measure between a template of the target and the current frame. The second tracking method is a probabilistic method which uses a state-space representation of a moving object to model its underlying dynamics. In this method, the tracking problem is viewed as a Bayesian interference problem. Examples are Kalman filter and particle filters. The advantage of the second tracking method is, it avoids the bulk work for segmentation and searching in the full image. There are three key steps in this process, prediction, matching (data association) and correction (updating). Tracking involves two basic steps motion and matching. Motion predicts the limited search region in which the tracked object most likely to be in the next frame. Matching identifies the object within the designed search region. Combining the detection methods with estimation filters can easily improve the tracking result. The Kalman filter has been used as estimation method for object and people tracking BIB001 , BIB003 , BIB004 . Authors in BIB002 have proposed a combination of HOG and Kalman filter for efficient human detection and tracking in a video. The detections can be improved by applying a Kalman filtered modeled using the standard equations of motions for constant velocity and acceleration. Considering the centroid of the tracking as point object in motion from one frame to the next frames, the displacement can be expressed using equations 1 and 2 for constant velocity and acceleration in 1D. (1) Since the tracking is in 2D, the above equation can be easily adopted to fit with the 2D motion model by decomposing the centroid r into its x and y component. In this case of constant velocity, we will have 4x4 transition matrix representing the displacement and velocity in x and y. At the same time for constant acceleration, the transition matrix will be 6x6 representing displacement, velocity and acceleration in x and y directions. The state transition and observation matrices for constant velocity model are given in equation 3 and 4. (3) A 6x6 matrix for state transition and 2x6 matrix for observation are used in the acceleration model in a similar manner as the equations of the constant velocity model. Process noise and the measurement noise matrices are assumed to be identity. The second order Kalman filter (position, velocity, and acceleration) is used to model the motion of each person in the scene. The Kalman filter works in two stages: prediction and correction. Taking the HOG detection and CAMshift as a measurement/observation for the Kalman filter, the next possible detection is predicted using the state transition matrix derived from the equation of motion model. The challenge comes when the person under tracking is missed after some time. In this case there will be only prediction without any update. The difference between constant velocity and acceleration comes in this step. From our experiment, the acceleration model gives better prediction result when the person is detected. On the other hand, the constant velocity model performs better in prediction when the person is missed in the middle. The overall MSE of both detectors before and after Kalman filter is given in 