Browsing by Author "Alzoubi, Alaa"

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    PotDataset
    (Cranfield University, 2018-04-10 10:07) Alzoubi, Alaa
    Pothole Image Dataset: We have developed a new image dataset (PotDataset) for pothole recognition task. The dataset includes images of pothole objects and non-pothole objects. The images were manually annotated, and used to evaluate our recognition method.
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    Towards scene understanding implementing the stixel world
    (IEEE, 2019-03-07) Grenier, Amélie; Alzoubi, Alaa; Feetham, Luke; Nam, David
    In this paper, we present our work towards scene understanding based on modeling the scene prior to understanding its content. We describe the environment representation model used, the Stixel World, and its benefits for compact scene representation. We show our preliminary results of its application in a diverse environment and the limitations reached in our experiments using imaging systems. We argue that this method has been developed in an ideal scenario and does not generalise well to uncommon changes in the environment. We also found that this method is sensitive to the quality of the stereo rectification and the calibration of the optics, among other parameters, which makes it time-consuming and delicate to prepare in real-time applications. We think that pixel-wise semantic segmentation techniques can address some of the shortcomings of the concept presented in a theoretical discussion.
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    Vehicle Obstacle Interaction Dataset (VOIDataset)
    (Cranfield University, 2018-10-11 13:26) Alzoubi, Alaa; Nam, David
    Vehicle-Obstacle Interaction Dataset (VOIDataset) includes 277 trajectories (sequences of x,y positions of the vehicle and the obstacle) of three different scenarios (67 crash, 106 left-pass, and 104 right-pass trajectories). The distance between the vehicle and the obstacle (length of the trajectory) is 50 meters. The trajectories were manually annotated, and used to evaluate our activity recognition method. Data was gathered using a simulation environment developed in Virtual Battlespace 3 (VBS3), with the Logitech G29 Driving Force Racing Wheel and pedals. Here a model of a Dubai highway was used. We consider a six lane road with an obstacle in the centre lane. The experiment consisted of 40 participants, all of varying ages, genders and driving experiences. Participants were asked to use their driving experience to avoid the obstacle. A Skoda Octavia was used in all trails, and with maximum speed 50KPH. We recorded the obstacle and ego-vehicle's coordinates (the centre position of the vehicle), velocity, heading angle, and distance from each other. The generated trajectories were recorded at 10Hz. Version 2: no change to the dataset, but appending contact details for more information: Alaa Alzoubi: alaa.alzoubi@buckingham.ac.uk David Nam: d.nam@cranfield.ac.uk