An Investigation into Segmenting Traffic Images Using Various Types of Graph Cuts

In computer vision, graph cuts are a way of segmenting an image into multiple areas. Graphs are built using one node for each pixel in the image combined with two extra nodes, known as the source and the sink. Each node is connected to several other nodes using edges, and each edge has a specific weight. Using different weighting schemes, different segmentations can be performed based on the properties used to create the weights. The cuts themselves are performed using an implementation of a solution to the maximum flow problem, which is then changed into a minimum cut according to the max-flow/min-cut theorem. In this thesis, several types of graph cuts are investigated with the intent to use one of them to segment traffic images. Each of these variations of graph cut is explained in detail and compared to the others. Then, one is chosen to be used to detect traffic. Several weighting schemes based on grayscale value differences, pixel variances, and mean pixel values from the test footage are presented to allow for the segmentation of video footage into vehicles and backgrounds using graph cuts. Our method of segmenting traffic images via graph cuts is then tested on several videos of traffic in various lighting conditions and locations. Finally, we compare our proposed method to a similarly performing method: background subtraction

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 199

This bibliography lists 82 reports, articles, and other documents introduced into the NASA scientific and technical information system in October 1979

’Eyes free’ in-car assistance: parent and child passenger collaboration during phone calls

This paper examines routine family car journeys, looking specifically at how passengers assist during a mobile telephone call while the drivers address the competing demands of handling the vehicle, interacting with various artefacts and controls in the cabin, and engage in co-located and remote conversations while navigating through busy city roads. Based on an analysis of video fragments, we see how drivers and child passengers form their conversations and requests around the call so as to be meaningful and paced to the demands, knowledge and abilities of their cooccupants, and how the conditions of the road and emergent traffic are oriented to and negotiated in the context of the social interaction that they exist alongside. The study provides implications for the design of car-based collaborative media and considers how hands- and eyesfree natural interfaces could be tailored to the complexity of activities in the car and on the road

PanDA: Panoptic Data Augmentation

The recently proposed panoptic segmentation task presents a significant challenge of image understanding with computer vision by unifying semantic segmentation and instance segmentation tasks. In this paper we present an efficient and novel panoptic data augmentation (PanDA) method which operates exclusively in pixel space, requires no additional data or training, and is computationally cheap to implement. By retraining original state-of-the-art models on PanDA augmented datasets generated with a single frozen set of parameters, we show robust performance gains in panoptic segmentation, instance segmentation, as well as detection across models, backbones, dataset domains, and scales. Finally, the effectiveness of unrealistic-looking training images synthesized by PanDA suggest that one should rethink the need for image realism for efficient data augmentation

Robust pedestrian detection and tracking in crowded scenes

In this paper, a robust computer vision approach to detecting and tracking pedestrians in unconstrained crowded scenes is presented. Pedestrian detection is performed via a 3D clustering process within a region-growing framework. The clustering process avoids using hard thresholds by using bio-metrically inspired constraints and a number of plan view statistics. Pedestrian tracking is achieved by formulating the track matching process as a weighted bipartite graph and using a Weighted Maximum Cardinality Matching scheme. The approach is evaluated using both indoor and outdoor sequences, captured using a variety of different camera placements and orientations, that feature significant challenges in terms of the number of pedestrians present, their interactions and scene lighting conditions. The evaluation is performed against a manually generated groundtruth for all sequences. Results point to the extremely accurate performance of the proposed approach in all cases

Gesture bike: examining projection surfaces and turn signal systems for urban cycling

Interactive surfaces could be employed in urban environments to make people more aware of moving vehicles, showing drivers’ intention and the subsequent position of vehicles. To explore the usage of projections while cycling, we created a system that displays a map for navigation and signals cyclist intention. The first experiment compared the task of map navigation on a display projected on a road surface in front of the bicycle with a head-up-display (HUD) consisting of a projection on a windshield. The HUD system was considered safer and easier to use. In our second experiment, we used projected surfaces to implement concepts inspired by Gibson’s perception theory of driving that were combined with detection of conventional cycling gestures to signal and visualize turning intention. The comparison of our system with an off-the-shelf turn signal system showed that gesture input was easier to use. A web-based follow-up study based on the recording of the two signalling systems from the perspective of participants in traffic showed that with the gesture-projector system it was easier to understand and predict the cyclist intention