In an effort to reduce driver errors in being the major cause of traffic accidents, there is a lot of research being conducted into the... Show moreIn an effort to reduce driver errors in being the major cause of traffic accidents, there is a lot of research being conducted into the development of advanced driver assistance systems (ADAS). ADAS is a system aimed at helping the driver in tasks such as pedestrian and vehicle detection, traffic sign recognition and lane detection. Pedestrian detection is one of the major tasks in advanced driver assistance systems (ADAS). Most of the stereo based pedestrian detection algorithms include three major steps: 1. Ground plane estimation 2. Region of interest (ROI) generation 3. Pedestrian classification In this thesis, we present a stereo-based pedestrian detection framework for advanced driver assistance systems by exploiting both color and depth information obtained from a stereo camera installed in a vehicle. In our proposed framework, we first use the vertical gradient of the dense depth map to estimate and discard the ground plane. The boundaries of the ground plane are then searched to detect the pedestrians and the depth values of the boundaries are used to compute the size of the detection windows for detecting pedestrians at different scales. In addition, a depth-based multi-scale ROI extraction method has been proposed to reduce the computation time of ROI extraction. For classifying ROIs to pedestrian and non-pedestrian classes, Histogram of Oriented Gradients (HOG)/Linear support vector machine (SVM) and Integral Channel Features (ICF)/Adaboost are used. To recover the missed pedestrians and improve the detection rate, an ROI tracking algorithm is proposed which incorporates the ROIs extracted from the current frame with theROIs tracked from a reference frame. For additional reduction in search space, we propose a novel algorithm to reduce the number of candidate windows extracted as ROI by taking advantage of the temporal correlation between the adjacent frames. We also propose a method to improve the accuracy of the pedestrian classifi- cation using the aggregated channel features. In this approach, we use the aggregated channel features as our baseline detector and improve it's accuracy using the tanh normalization and Gabor filter. After classification using Adaboost, we use a posi- tive subset of the bounding boxes to classify them again using Convlutional Neural Network to finalize the detection. Ph.D. in Electrical Engineering, May 2017 Show less