Tarek Sayed

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We introduce a graphical framework for multiple instance learning (MIL) based on Markov networks. This framework can be used to model the traditional MIL definition as well as more general MIL definitions. Different levels of ambiguity – the portion of positive instances in a bag – can be explored in weakly supervised data. To train these models, we propose(More)
Recent research advocates the use of count models with random parameters as an alternative method for analyzing accident frequencies. In this paper a dataset composed of urban arterials in Vancouver, British Columbia, is considered where the 392 segments were clustered into 58 corridors. The main objective is to assess the corridor effects with alternate(More)
— The importance of reducing the social and economic costs associated with traffic collisions can not be overstated. The first goal of this research is to develop a method for automated road safety analysis using video sensors in order to address the problem of a dependency on the deteriorating collision data. The method will automate the extraction of(More)
We introduce MMTrack (max-margin tracker), a single-target tracker that linearly combines constant and adaptive appearance features. We frame offline single-camera tracking as a structured output prediction task where the goal is to find a sequence of locations of the target given a video. Following recent advances in machine learning, we discriminatively(More)
In this paper, we introduce MMTrack, a hybrid single pedestrian tracking algorithm that puts together the advantages of descriptive and discriminative approaches for tracking. Specifically, we combine the idea of cluster-based appearance modeling and online tracking and employ a max-margin criterion for jointly learning the relative importance of different(More)
This paper advocates the use of multivariate Poisson-lognormal (MVPLN) regression to develop models for collision count data. The MVPLN approach presents an opportunity to incorporate the correlations across collision severity levels and their influence on safety analyses. The paper introduces a new multivariate hazardous location identification technique,(More)
The main challenge in conducting observational before-after (BA) studies of road safety measures is to use a methodology that accounts for many potential confounding factors. However, it is usually difficult to evaluate and decide on the accuracy of the different safety evaluation techniques available in literature. This is mainly because the outcome of the(More)
Full Bayes linear intervention models have been recently proposed to conduct before-after safety studies. These models assume linear slopes to represent the time and treatment effects across the treated and comparison sites. However, the linear slope assumption can only furnish some restricted treatment profiles. To overcome this problem, a first-order(More)
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