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IMPROVING PEDESTRIAN DETECTION USING OPTICAL FLOW
Title
IMPROVING PEDESTRIAN DETECTION USING OPTICAL FLOW
Creator
Kong, Lingxing
Date
2015, 2015-12
Description
Pedestrian detection, which has wide applications on surveillance, automatic driving and robotics, plays a significant role in computer vision...
Show morePedestrian detection, which has wide applications on surveillance, automatic driving and robotics, plays a significant role in computer vision. Among all kinds of pedestrian detection methods, stereo based method achieves an accurate and efficient detection result by exploiting depth and color information. However, many stereo based systems fail at considering motion information which is important in locating and detecting an object. For many pedestrian detection systems, adding extra data like motion is one of the most effective ways to improve the performance. Therefore, this thesis proposes a multi-cue pedestrian detection system which integrates optical flow based and stereo based modules for combining motion, depth and color information. In the proposed system, optical flow and disparity value are estimated by using the frames which obtained from a stereo camera. In order to obtain accurate pedestrian motion, ego motion is compensated by using motion clustering, affine model and RANSAC. After that, the motion and the depth information are exploited for ROI generation. Finally, SVM is trained by the combination of motion feature and HOG feature. Experimental results show that the use of high-accuracy optical flow along with depth and color information improves the performance of multi-cue pedestrian detection system.
M.S. in Electrical Engineering, December 2015
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PEDESTRIAN DETECTION AND TRACKING FOR ADVANCED DRIVER ASSISTANCE SYSTEMS
Title
PEDESTRIAN DETECTION AND TRACKING FOR ADVANCED DRIVER ASSISTANCE SYSTEMS
Creator
Mesmakhosroshahi, Maral
Date
2017, 2017-05
Description
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
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