With the recent developments in Computer Vision and Pattern Recognition, surveillance applications are equipped with the capabilities of event/activity understanding and interpretation which usually require recognizing humans in real world scenes. Real world scenes such as airports, streets and train stations are complex because they involve many people, complicated occlusions and cluttered backgrounds. Although complex real world scenes exist, human detectors have the capability to locate pedestrians accurately even in complex scenes and visual trackers have the capability to track targets in cluttered environments. The integration of visual object detection and tracking, which are the fundamental features of available surveillance applications, is one of the solutions for multi-human tracking problem in crowded scenes which is studied in this thesis. In this thesis, human body part detectors, which are capable of detecting human heads and human upper body parts, are trained with Support Vector Machines (SVM) by using Histogram of Oriented Gradients (HOG), which is one of the state-of-the-art descriptor for human detection. The training process is elaborated by investigating the effects of the parameters of the HOG descriptor. The human heads and upper body parts are searched in the region of interests (ROI) computed by detecting motion. In addition, these human body part detectors are integrated with a multi-human tracker which solves the data association problem with the Multi Scan Markov Chain Monte Carlo Data Association (MCMCDA) algorithm. Associated measurements of human upper body part locations are used for state correction for each track. State estimation is done through Kalman Filter. The performance of detectors are evaluated using MIT Pedestrian dataset and INRIA Human dataset.

In recent years, the utilization of 3D display systems became popular in many application areas. One of the most important issues in the utilization of these systems is to render the correct view to the observer based on his/her position. In this thesis, we propose and implement a single user view rendering system for autostereoscopic/stereoscopic displays. The system can easily be installed on a standard PC together with an autostereoscopic display or stereoscopic glasses (shutter, polarized, pulfrich, and anaglyph) with appropriate video card. Proposed system composes of three main blocks: view point detection, view point tracking and intermediate view reconstruction. Haar object detection method, which is based on boosted cascade of simple feature classifiers, is utilized as the view point detection method. After detection, feature points are found on the detected region and accordingly they are fed to the feature tracker. View point of the observer is calculated by using the tracked position of the observer on the image. Correct stereoscopic view is, then, rendered on the display. A 3D warping-based method is utilized in the system as the intermediate view reconstruction method. System is implemented on a computer with Pentium IV 3.0 GHz processor using E-D 3D shutter glasses and Creative NX Webcam.

Visual target tracking problem consists of two topics: Obtaining targets from camera measurements and target tracking. Even though it has been studied for more than 30 years, there are still some problems not completely solved. Especially in the case of multiple targets, association of measurements to targets, creation of new targets and deletion of old ones are among those. What is more, it is very important to deal with the occlusion and crossing targets problems suitably. We believe that a slightly modified version of multiple hypothesis tracking can successfully deal with most of the aforementioned problems with sufficient success. Distance, track size, track color, gate size and track history are used as parameters to evaluate the hypotheses generated for measurement to track association problem whereas size and color are used as parameters for occlusion problem. The overall tracker has been fine tuned over some scenarios and it has been observed that it performs well over the testing scenarios as well. Furthermore the performance of the tracker is analyzed according to those parameters in both association and occlusion handling situations.

Visual target tracking problem consists of two topics: Obtaining targets from camera measurements and target tracking. Even though it has been studied for more than 30 years, there are still some problems not completely solved. Especially in the case of multiple targets, association of measurements to targets, creation of new targets and deletion of old ones are among those. What is more, it is very important to deal with the occlusion and crossing targets problems suitably. We believe that a slightly modified version of multiple hypothesis tracking can successfully deal with most of the aforementioned problems with sufficient success. Distance, track size, track color, gate size and track history are used as parameters to evaluate the hypotheses generated for measurement to track association problem whereas size and color are used as parameters for occlusion problem. The overall tracker has been fine tuned over some scenarios and it has been observed that it performs well over the testing scenarios as well. Furthermore the performance of the tracker is analyzed according to those parameters in both association and occlusion handling situations.

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In this thesis, novel methods for background modeling, tracking, occlusion handling and event recognition via multi-camera configurations are presented. As the initial step, building blocks of typical single camera surveillance systems that are moving object detection, tracking and event recognition, are discussed and various widely accepted methods for these building blocks are tested to asses on their performance. Next, for the multi-camera surveillance systems, background modeling, occlusion handling, tracking and event recognition for two-camera configurations are examined. Various foreground detection methods are discussed and a background modeling algorithm, which is based on multi-variate mixture of Gaussians, is proposed. During occlusion handling studies, a novel method for segmenting the occluded objects is proposed, in which a top-view of the scene, free of occlusions, is generated from multi-view data. The experiments indicate that the occlusion handling algorithm operates successfully on various test data. A novel tracking method by using multi-camera configurations is also proposed. The main idea of multi-camera employment is fusing the 2D information coming from the cameras to obtain a 3D information for better occlusion handling and seamless tracking. The proposed algorithm is tested on different data sets and it shows clear improvement over single camera tracker. Finally, multi-camera trajectories of objects are classified by proposed multi-camera event recognition method. In this method, concatenated different view trajectories are used to train Gaussian Mixture Hidden Markov Models. The experimental results indicate an improvement for the multi-camera event recognition performance over the event recognition by using single camera.

This thesis deals with an automatic counting of cylinders placed on the back of a truck using images taken by a camera mounted above the car. To achieve this goal, an SVM classifier based on HOG image descriptors has been trained to detect the cylinders. Further, a tracking method based on optical flow estimation has been designed to track the cylinders through image sequences. The result of the thesis is an application that counts bottles with precision 93,08 % placed on the truck and visualizes results of the detection.

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