This thesis presents a general, trainable system for object detection in static images and video sequences. The core system finds a certain class of objects in static images of completely unconstrained, cluttered scenes without using motion, tracking, or handcrafted models and without making any assumptions on the scene structure or the number of objects in the scene. The system uses a set of training data of positive and negative example images as input, transforms the pixel images to a Haar wavelet representation, and uses a support vector machine classifier to learn the difference between in-class and out-of-class patterns. To detect objects in out-of-sample images, we do a brute force search over all the subwindows in the image. This system is applied to face, people, and car detection with excellent results. For our extensions to video sequences, we augment the core static detection system in several ways -- 1) extending the representation to five frames, 2) implementing an approximation to a Kalman filter, and 3) modeling detections in an image as a density and propagating this density through time according to measured features. In addition, we present a real-time version of the system that is currently running in a DaimlerChrysler experimental vehicle. As part of this thesis, we also present a system that, instead of detecting full patterns, uses a component-based approach. We find it to be more robust to occlusions, rotations in depth, and severe lighting conditions for people detection than the full body version. We also experiment with various other representations including pixels and principal components and show results that quantify how the number of features, color, and gray-level affect performance.

This thesis presents a learning based approach for detecting classes of objects and patterns with variable image appearance but highly predictable image boundaries. It consists of two parts. In part one, we introduce our object and pattern detection approach using a concrete human face detection example. The approach first builds a distribution-based model of the target pattern class in an appropriate feature space to describe the target's variable image appearance. It then learns from examples a similarity measure for matching new patterns against the distribution-based target model. The approach makes few assumptions about the target pattern class and should therefore be fairly general, as long as the target class has predictable image boundaries. Because our object and pattern detection approach is very much learning-based, how well a system eventually performs depends heavily on the quality of training examples it receives. The second part of this thesis looks at how one can select high quality examples for function approximation learning tasks. We propose an {em active learning} formulation for function approximation, and show for three specific approximation function classes, that the active example selection strategy learns its target with fewer data samples than random sampling. We then simplify the original active learning formulation, and show how it leads to a tractable example selection paradigm, suitable for use in many object and pattern detection problems.

This paper describes a general, trainable architecture for object detection that has previously been applied to face and peoplesdetection with a new application to car detection in static images. Our technique is a learning based approach that uses a set of labeled training data from which an implicit model of an object class -- here, cars -- is learned. Instead of pixel representations that may be noisy and therefore not provide a compact representation for learning, our training images are transformed from pixel space to that of Haar wavelets that respond to local, oriented, multiscale intensity differences. These feature vectors are then used to train a support vector machine classifier. The detection of cars in images is an important step in applications such as traffic monitoring, driver assistance systems, and surveillance, among others. We show several examples of car detection on out-of-sample images and show an ROC curve that highlights the performance of our system.

The goal of this thesis is to develop better practical methods for detecting common object classes in real world images. We present a family of object detectors that combine Histogram of Oriented Gradient (HOG), Local Binary Pattern (LBP) and Local Ternary Pattern (LTP) features with efficient Latent SVM classifiers and effective dimensionality reduction and sparsification schemes to give state-of-the-art performance on several important datasets including PASCAL VOC2006 and VOC2007, INRIA Person and ETHZ. The three main contributions are as follows. Firstly, we pioneer the use of Local Ternary Pattern features for object detection, showing that LTP gives better overall performance than HOG and LBP, because it captures both rich local texture and object shape information while being resistant to variations in lighting conditions. It thus works well both for classes that are recognized mainly by their structure and ones that are recognized mainly by their textures. We also show that HOG, LBP and LTP complement one another, so that an extended feature set that incorporates all three of them gives further improvements in performance. Secondly, in order to tackle the speed and memory usage problems associated with high-dimensional modern feature sets, we propose two effective dimensionality reduction techniques. The first, feature projection using Partial Least Squares, allows detectors to be trained more rapidly with negligible loss of accuracy and no loss of run time speed for linear detectors. The second, feature selection using SVM weight truncation, allows active feature sets to be reduced in size by almost an order of magnitude with little or no loss, and often a small gain, in detector accuracy. Despite its simplicity, this feature selection scheme outperforms all of the other sparsity enforcing methods that we have tested. Lastly, we describe work in progress on Local Quantized Patterns (LQP), a generalized form of local pattern features that uses lookup table based vector quantization to provide local pattern style pixel neighbourhood codings that have the speed of LBP/LTP and some of the flexibility and power of traditional visual word representations. Our experiments show that LQP outperforms all of the other feature sets tested including HOG, LBP and LTP.

The purpose of this study was the hardware implementation of a real time moving object contour extraction.Segmentation of image frames to isolate moving objects followed by contour extraction using digitalmorphology was carried out in this work. Segmentation using temporal difference with median thresholdingapproach was implemented, experimental methods were used to determine the suitable morphological operatorsalong with their structuring elements dimensions to provide the optimum contour extraction.The detector with image resolution of 1280 x1024 pixels and frame rate of 60 Hz was successfully implemented,the results indicate the effect of proper use of morphological operators for post processing and contourextraction on the overall efficiency of the system. An alternative segmentation method based on Stauffer & Grimson algorithm was investigated and proposed which promises better system performance at the expense ofimage resolution and frame rate

The purpose of this thesis is to investigate the applicability of a certain model based classification algorithm. The algorithm is centered around a flexible wireframe prototype that can instantiate a number of different vehicle classes such as a hatchback, pickup or a bus to mention a few. The parameters of the model are fitted using Newton minimization of errors between model line segments and observed line segments. Furthermore a number of methods for object detection based on motion are described and evaluated. Results from both experimental and real world data is presented.

Successful and high precision localization is an important feature for autonomous vehicles in an urban environment. GPS solutions are not good on their own and laser, sonar and radar are often used as complementary sensors. Localization with these sensors requires the use of techniques grouped under the acronym SLAM (Simultaneous Localization And Mapping). These techniques work by comparing the current sensor inputs to either an incrementally built or known map, also adding the information to the map.Most of the SLAM techniques assume the environment to be static, which means that dynamics and clutter in the environment might cause SLAM to fail. To ob-tain a more robust algorithm, the dynamics need to be dealt with. This study seeks a solution where measurements from different points in time can be used in pairwise comparisons to detect non-static content in the mapped area. Parked cars could for example be detected at a parking lot by using measurements from several different days.The method successfully detects most non-static objects in the different test datasets from the sensor. The algorithm can be used in conjunction with Pose-SLAM to get a better localization estimate and a map for later use. This map is good for localization with SLAM or other techniques since only static objects are left in it.

A DSP-based system is developed in this thesis for tracking ¡§an arbitrary-shaped object¡¨. It uses CCD camera to capture images, and detects in the video sequence. When we want to track a target that we interest, we can make the target in the view of camera. If the target move, the system will lock it and extract its contour by using active contour model. After extracting contour, the system will start to track target and shows the locked image on the LCD screen. The tracking system includes three sub-systems : ¡§Moving Object Detection¡¨, ¡§Active Contour Model¡¨, and ¡§Contour Matching¡¨. From the results of experiment, it can meet the expectation and gain good performance and robustness.

<p>The purpose of this thesis is to investigate the applicability of a certain model based classification algorithm. The algorithm is centered around a flexible wireframe prototype that can instantiate a number of different vehicle classes such as a hatchback, pickup or a bus to mention a few. The parameters of the model are fitted using Newton minimization of errors between model line segments and observed line segments. Furthermore a number of methods for object detection based on motion are described and evaluated. Results from both experimental and real world data is presented.</p>

Considering todays growing society and developing technologies which are co-influential between each other, there is a larger scope of security concerns, traffic congestion due to improper planning and hence a greater need of more intelligent video surveillance. In this thesis, we have worked on developing such intelligent video surveillance system which mainly focusses on cell area such as parking spaces. The system operates on outdoor environment with a stationary camera; the main objective of this system is detecting and tracking of moving objects mainly cars. Two detection algorithms were developed using optical flow as core strategy. In the first algorithm the flow vectors were classified based on their magnitude and orientation; the GOMAG algorithm. The second algorithm used K-means method on the flow vectors to achieve the classification for moving object detection; the SKMO algorithm. A comparison analysis was done between the proposed algorithms and well known detection algorithms of background modeling and Otsu’s segmentation of flow vectors. The both proposed algorithms performed significantly better than background modeling and Otsu’s segmentation of flow vectors algorithms. The SKMO algorithm showed better stability and processed time efficiency than the GOMAG algorithm.