HUMAN TRACKING SYSTEM Kavita Vilas Wagh* *PG Student, Electronics & Telecommunication Department, Vivekanand Institute of Technology, Mumbai, India waghkav@gmail.com Dr. R.K. Kulkarni** **Professor, Electronics and Telecommunitaion Department Vivekanand Institute of Technology, Mumbai, INDIA rk1_2002@yahoo.com ABSTRACT Human tracking is a comprehensive framework for tracking coarse human model performed from sequences of synchronized monocular grayscale images in single or multiple camera system coordinates. It is nothing but segmenting an interested human from video scene and keep track if it continuously. It demonstrates the feasibility of an end to end person tracking system where initially it start background subtraction, then detection of the interested human and tracking of that human form one frame to another continuously. For detection of the interested human PCA algorithm is used. Finally Kalman filter is introduced into tracking the people. Our system have demonstrated that as compaired with other methods it reduces detection time comparitively and improves human detection and tracking accuracy. Keywords Segmentation, tracking, Detection, Eigenfaces. I. INTRODUCTION Tracking motion is of interest in numerous applications such as surveillance, robot vision, traffic monitoring, animation, detection of human, vehicle, tennis ball i.e. in sports, and analysis of athletic performance and content-based management of digital image database [2]. There are various methods introduced for object tracking previously. But to detect the object and to track it continuously is find to be very much critical task and it does not give 100% correct results. The proposed system consist of, a comprehensive framework for tracking coarse human models across multiple coordinates and demonstrates the feasibility of an end to end person tracking system with existing techniques in segmentation, motion detection and tracking[1]. There are different techniques for background subtraction. In the proposed system Gaussian distribution based background subtraction is used. For detection of targeted human principal component analysis is the preferred one which is based on eigenfaces. i.e Fisher face recognition. For continuous tracking of targeted human there are number of algorithms like Camshaft algorithm, meanshift algorithm, Kalman algorithm. Within all the algorithms it is analyzed that Kalman algorithm is seem to be one which is the best suited for continuous tracking from frame to frame.i.e. it reduces noise which gives almost 100 % results. II. OPERATION In the proposed system initially there is a video by cctv camera, the camera may be in the different formats. This video is then converted into number of frames. Then matching of these frames with the original image is performed. There are three steps in the proposed system. Initially background subtraction performed using Gaussian distribution method. Second step is detection of human using PCA algorithm. Then tracking of targeted human using Kalman algorithm is performed. As shown in Fig.1. 1
Fig.1: Basic procedure of tracking. A. Background subtraction Object detection can be achieved by building a representation of the scene called background model and then finding deviation from the model for each incoming frame. Any significant change in an image region from the background model signifies the moving object. The pixels constituting the region undergoing changes are marked for further processing. Usually, a connected component algorithm is applied to obtain connected regions corresponding to the objects. And this process is referred to as background subtraction[5]. A substantial improvement in background modeling is achieved by using multimodal Statistical models to describe per-pixel background color by using a mixture of Gaussian to model the pixel color. Where pixel in the current frame is checked against the background model by comparing it with every Gaussian in the model until a matching Gaussian is found. If the match is found, the mean and variance of the matched Gaussian is updated, otherwise a new Gaussian with the mean equal to the current pixel color and some initial variance is introduced into the mixture. Each pixel is classified based on whether the matched distribution represents the background process [4]. Moving regions, which are detected using this approach, along with the background models are shown in Fig 2. Fig. 2. Eigenspace decomposition-based background subtraction [4] (a) An input image with objects, (b) Reconstructed image after projecting input image onto the eigenspace, (c) Difference image. A. Detection of targeted human In the proposed system for detection of targeted human PCA is used. PCA is a method of transforming number of correlated variables into a smaller number of uncorrelated variables. It decomposes a signal or an image into a set of additive orthogonal basis vectors or say eigenvectors. It can applied to the task of face recognition by converting the pixels of an image into a number of eigenface feature vectors, which can then be measure the similarity of two face images[3] 2
Steps for detection using PCA algorithm are: I. Initially obtain the zeromean face images. II. Calculate the eigenvectors and eigenvalues of the eigenfaces III. The output from previous step is a ` matrix of eigenvectors. VI.Truncate the eigenvector matrix to maximum principal components. V. Project the mean-shifted input images into the subspace defined by truncated set of eigenvectors. VI.Find Euclidean distance between their corresponding feature vector. VII.T he smaller the distance between the feature vectors and more similar the faces. VIII. It is defined by similarity score based on the inverse Euclidean distance. IX. The similarity score is calculated between an input face image and each of the training images. X. The matched face is the one with the highest similarity. then the human is the targeted human otherwise not. i.e if Euclidean distance is less than the threshold given then target is detected by green spot otherwise not and targeted is approximated by red spot. B. Kalman Filter The proposed work consists of tracking of the detected targeted human which is performed by Kalman filter, which estimates the position of the target in each frame of the sequence. The position of the object in an image at time k, the size of the object, width and length of the search window of the object vary due to the mobility of the object during the sequence. These parameters represent the state vector and measurement vector of the Kalman filter. It uses a series of measurements observed over time, containing noise and other inaccuracies, and produces estimates of unknown variables that tend to be more precise than those based on a single measurement alone[8]. I. For prediction step, as shown in Fig.4 the Kalman filter produces estimates of the current state variables. II. Once the outcome of the next measurement is observed these estimates are updated. Fig.3 Targeting Humen So, as shown in the Fig.3 the criteria used in the proposed system is 0.0039.i.e. if D <= 0.0039 Fig.4. Kalman prediction. The Kalman filter model assumes the true state at time k is evolved from the state at (k 1) 3
. Where, F k is the state transition model which is applied to the previous state x k 1. B k is the control-input model which is applied to the control vector u k. w k is the process noise. III. RESULT: Ensemble tracking is a general framework for tracking human with Kalman filter. Where detection of targeted object is performed by PCA algorithm based eigenface recognition. The video is detected from the cctv camera which are in the form of.mov format. This video is converted into number of frames. So, there are three fields in the resulted output window as shown in Fig.5. First window consist of the original frame form from the video. In the second window tracking of moving body is performed using kalman algorithm. Here all the moving human being are tracked and mark with red shade. In the third window the targeted human is matched with the original frame, this matching is performed using PCA algorithm as shown in the Fig.5,Fig.6. While in Fig.7 the targeted human is not exactly in the frame so it is not detected. Fig.5 Targeted human is marked with green spot and approximated human are marked with red spot. Fig.6 Fig.7 In this frame targeted human is not exactly in the frame so it is not detected. Same system is tried with different video. Where targeted human is exactly detected as in Fig.8 and Fig.10. while in Fig.9 targeted human is not in the frame so it is not detected 4
Fig.8. System tried with some other videos where targeted person is marked with green spot Fig.9 Again targeted person is not in the frame so not detected Fig.10 IV. FUTURE SCOPE: The proposed system consists of human tracking with single view. So in future it is possible to design a system using multiple angles or views. Even it is possible to design it with multiple switching techniques. CONCLUSION. The proposed work focuses on simultaneous tracking of human. During the test sequence generated with different methods of pre-processing, it is concluded that the tracking of human differs from one frame to another and several parameters can affect the result of tracking. Experimental results show that the algorithm for tracking i.e. the kalman filter is superior in terms of precision, reliability and execution time. The eigenface based PCA algorithm is seem to be the most beneficial algorithm for detection of the coarse human as it detects the correct person almost every time. In particular, the use of several methods of preprocessing to detect the human in each frame of the sequence provides satisfactory results in the case of the complex video sequences. REFERENCES [1] Q.cai and J.K.Agrawal, Human tracking motion in structured environment using distributed camera system, IEEE transactions on pattern analysis on machine intelligence, volume 21, number 12, november 1999 [2]. J.Wang, F.He and X.Zhang and G.Yun, Tracking Object through Occlusions Using Improved Kalman Filter, number 6, 2010. [3]. Matthew Turk, Alex Pentland, Eigenfaces for Recognition, Journal of cognitive Neuroscience, volume 3, number 1. [4]. Alper Yilmaz, Omer Javed, Mubarak Shah, Object Tracking: A survey, ACM Computing Survey, Volume 38, Number 4, Article 13, December 2006. 5
[5]. G. bradski, and R. Gary, and L.M. Olga, Computer Vision Face Tracking for Use in Perceptual User Interface, Intel Technology Journal, volume 10, number 5, 1998. [6] G. Bradski and T.Ogiuchi, and M. Higashikubo Visual Tracking Algorithm using Pixel-Pair Feature, International Conference on Pattern Recognition, number 4, 2010. [7]. Y. Ruiguo, and Z. Xinrong, The Design and Implementation of Face Tracking in Real Time Multimedia Recording System IEEE Transaction, number 3, 2009. [8]. Shengluan Huang, Jingxin Hong, Moving Object Tracking System Based On Camshift And Kalman Filter, 2011 IEEE. 6