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— This work aims at addressing the many problems that have hindered the development of vision-based systems for automated road safety analysis. The approach relies on traffic conflicts used as surrogates for collision data. Traffic conflicts are identified by computing the collision probability for any two road users in an interaction. A complete system is(More)
Pedestrian data collection is critical for the planning and design of pedestrian facilities. Most pedestrian data collection efforts involve field observations or observer-based video analysis. These manual observations are time consuming, limited in coverage, resource intensive and error prone. Automated video analysis which involves the use of computer(More)
1 Traditional road safety diagnosis is a reactive approach, based on historical collision data. This work aims at developing a proactive approach which avoids waiting for accidents to happen. There is a need for surrogate safety measures that can also provide complementary information and are easy to collect, e.g. requiring shorter observation periods. The(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)
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)
— 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)