Visual Clutter Study for Pedestrian Using Large Scale Naturalistic Driving Data

Some of the pedestrian crashes are due to driver’s late or difficult perception of pedestrian’s appearance. Recognition of pedestrians during driving is a complex cognitive activity. Visual clutter analysis can be used to study the factors that affect human visual search efficiency and help design advanced driver assistant system for better decision making and user experience. In this thesis, we propose the pedestrian perception evaluation model which can quantitatively analyze the pedestrian perception difficulty using naturalistic driving data. An efficient detection framework was developed to locate pedestrians within large scale naturalistic driving data. Visual clutter analysis was used to study the factors that may affect the driver’s ability to perceive pedestrian appearance. The candidate factors were explored by the designed exploratory study using naturalistic driving data and a bottom-up image-based pedestrian clutter metric was proposed to quantify the pedestrian perception difficulty in naturalistic driving data. Based on the proposed bottom-up clutter metrics and top-down pedestrian appearance based estimator, a Bayesian probabilistic pedestrian perception evaluation model was further constructed to simulate the pedestrian perception process

Compound Models for Vision-Based Pedestrian Recognition

This thesis addresses the problem of recognizing pedestrians in video images acquired from a moving camera in real-world cluttered environments. Instead of focusing on the development of novel feature primitives or pattern classifiers, we follow an orthogonal direction and develop feature- and classifier-independent compound techniques which integrate complementary information from multiple image-based sources with the objective of improved pedestrian classification performance. After establishing a performance baseline in terms of a thorough experimental study on monocular pedestrian recognition, we investigate the use of multiple cues on module-level. A motion-based focus of attention stage is proposed based on a learned probabilistic pedestrian-specific model of motion features. The model is used to generate pedestrian localization hypotheses for subsequent shape- and texture-based classification modules. In the remainder of this work, we focus on the integration of complementary information directly into the pattern classification step. We present a combination of shape and texture information by means of pose-specific generative shape and texture models. The generative models are integrated with discriminative classification models by utilizing synthesized virtual pedestrian training samples from the former to enhance the classification performance of the latter. Both models are linked using Active Learning to guide the training process towards informative samples. A multi-level mixture-of-experts classification framework is proposed which involves local pose-specific expert classifiers operating on multiple image modalities and features. In terms of image modalities, we consider gray-level intensity, depth cues derived from dense stereo vision and motion cues arising from dense optical flow. We furthermore employ shape-based, gradient-based and texture-based features. The mixture-of-experts formulation compares favorably to joint space approaches, in view of performance and practical feasibility. Finally, we extend this mixture-of-experts framework in terms of multi-cue partial occlusion handling and the estimation of pedestrian body orientation. Our occlusion model involves examining occlusion boundaries which manifest in discontinuities in depth and motion space. Occlusion-dependent weights which relate to the visibility of certain body parts focus the decision on unoccluded body components. We further apply the pose-specific nature of our mixture-of-experts framework towards estimating the density of pedestrian body orientation from single images, again integrating shape and texture information. Throughout this work, particular emphasis is laid on thorough performance evaluation both regarding methodology and competitive real-world datasets. Several datasets used in this thesis are made publicly available for benchmarking purposes. Our results indicate significant performance boosts over state-of-the-art for all aspects considered in this thesis, i.e. pedestrian recognition, partial occlusion handling and body orientation estimation. The pedestrian recognition performance in particular is considerably advanced; false detections at constant detection rates are reduced by significantly more than an order of magnitude

Gabor-enhanced histogram of oriented gradients for human presence detection applied in aerial monitoring

In UAV-based human detection, the extraction and selection of the feature vector are one of the critical tasks to ensure the optimal performance of the detection system. Although UAV cameras capture high-resolution images, human figures' relative size renders persons at very low resolution and contrast. Feature descriptors that can adequately discriminate between local symmetrical patterns in a low-contrast image may improve a human figures' detection in vegetative environments. Such a descriptor is proposed and presented in this paper. Initially, the acquired images are fed to a digital processor in a ground station where the human detection algorithm is performed. Part of the human detection algorithm is the GeHOG feature extraction, where a bank of Gabor filters is used to generate textured images from the original. The local energy for each cell of the Gabor images is calculated to identify the dominant orientations. The bins of conventional HOG are enhanced based on the dominant orientation index and the accumulated local energy in Gabor images. To measure the performance of the proposed features, Gabor-enhanced HOG (GeHOG) and other two recent improvements to HOG, Histogram of Edge Oriented Gradients (HEOG) and Improved HOG (ImHOG), are used for human detection on INRIA dataset and a custom dataset of farmers working in fields captured via unmanned aerial vehicle. The proposed feature descriptor significantly improved human detection and performed better than recent improvements in conventional HOG. Using GeHOG improved the precision of human detection to 98.23% in the INRIA dataset. The proposed feature can significantly improve human detection applied in surveillance systems, especially in vegetative environments

Detection of Motorcycles in Urban Traffic Using Video Analysis: A Review

Motorcycles are Vulnerable Road Users (VRU) and as such, in addition to bicycles and pedestrians, they are the traffic actors most affected by accidents in urban areas. Automatic video processing for urban surveillance cameras has the potential to effectively detect and track these road users. The present review focuses on algorithms used for detection and tracking of motorcycles, using the surveillance infrastructure provided by CCTV cameras. Given the importance of results achieved by Deep Learning theory in the field of computer vision, the use of such techniques for detection and tracking of motorcycles is also reviewed. The paper ends by describing the performance measures generally used, publicly available datasets (introducing the Urban Motorbike Dataset (UMD) with quantitative evaluation results for different detectors), discussing the challenges ahead and presenting a set of conclusions with proposed future work in this evolving area