Driver behavioural classification from trajectory data

Rigolli, Marco; Williams, Quentin; Gooding, Mark J.; Brady, Miranda J.

doi:10.1109/itsc.2005.1520168

Cited by 8 publications

(15 citation statements)

References 6 publications

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“…The underlying sub-process of traffic surveillance from video, such as detection of trajectory of vehicles provide outputs that are then utilized to extract highlevel representations of driving behaviors [7] [14]. However, the inferred driving models are specified to define a unique concept.…”

Section: Related Workmentioning

confidence: 99%

“…The first model provides similar functionality as [7] -detection of the aggressive driver; the second one creates a professional driver recognizer based on detection of taxis that have distinctive color.…”

Section: Driving Behavior Modelsmentioning

confidence: 99%

“…. , O m }, we first recognize the aggressive drivers using Bayesian Factor Analysis (BFA) [18] and select 20 positive observations forming a training exemplar (see [7] for the detailed implementation). We then estimate 20 sets of parameters of HMM using Baum-Welch algorithm and average the resulting parameters for computing the final transition and output probabilities.…”

Section: Driving Behavior Modelsmentioning

confidence: 99%

“…The application of many sophisticated computer vision techniques to the analysis of traffic video sequence facilitates considerable improvements to road traffic monitoring and surveillance data collection (We refer readers to [6] for an overall survey of video processing techniques in traffic applications). In order to infer the highlevel driving behavior model through the collection of lowlevel traffic video surveillance data, several researchers have proposed various methods recently [7] [8]. However, these methods are designed for a unique task (driver classification in [7] and mental state inference in [8]), the general representation of the data collected from traffic surveillance is missing in their work.…”

Section: Introductionmentioning

confidence: 99%

“…In order to infer the highlevel driving behavior model through the collection of lowlevel traffic video surveillance data, several researchers have proposed various methods recently [7] [8]. However, these methods are designed for a unique task (driver classification in [7] and mental state inference in [8]), the general representation of the data collected from traffic surveillance is missing in their work.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Driving behavior inference from traffic surveillance data

Zhou

Varadarajan

Vincze

et al. 2011

2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC)

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Autonomous extraction of information from traffic video surveillance has received widespread attention in recent years due to the capability of analyzing massive data persistently. In this paper, we present a system for extracting vehicle information from traffic video sequence build a framework for defining driver behavior models. We detect the sedans and split them into two categories -taxi and otherwise, according to the color and size. The labeled cars from two categories form two exemplars of driver behavior -professional and standard. We use these two exemplars to compute the unknown parameters of the Hidden Markov Model (HMM) and demonstrate the quantitative discrimination of these two categories, which can be used for many applications of intelligent transportation system, such as abnormality detection and transportation planning.We also demonstrate applications which utilize the proposed model to detect aggressive drivers and professional drivers. The system is extensible with large surveillance databases for automated detection of driver behavior and states in-situ for a variety of traffic event models. Furthermore, statistical information on driver behavior of different vehicle types, demographics and time periods for various locations of interest can be obtained by off-line processing of the surveillance data using the proposed model. The proposed framework can further be integrated with automated surveillance systems for guiding traffic controllers as well as city planners in better management of traffic.

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Section: Related Workmentioning

confidence: 99%

Section: Driving Behavior Modelsmentioning

confidence: 99%

Section: Driving Behavior Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Driving behavior inference from traffic surveillance data

Zhou

Varadarajan

Vincze

et al. 2011

2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC)

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WhozDriving: Abnormal Driving Trajectory Detection by Studying Multi-faceted Driving Behavior Features

Guo

Chen

et al. 2016

Big Data Computing and Communications

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Gimme Signals: Discriminative signal encoding for multimodal activity recognition

Memmesheimer

Theisen

Paulus

2020

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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We present a simple, yet effective and flexible method for action recognition supporting multiple sensor modalities. Multivariate signal sequences are encoded in an image and are then classified using a recently proposed Effi-cientNet CNN architecture. Our focus was to find an approach that generalizes well across different sensor modalities without specific adaptions while still achieving good results. We apply our method to 4 action recognition datasets containing skeleton sequences, inertial and motion capturing measurements as well as Wi-Fi fingerprints that range up to 120 action classes. Our method defines the current best CNN-based approach on the NTU RGB+D 120 dataset, lifts the state of the art on the ARIL Wi-Fi dataset by +6.78%, improves the UTD-MHAD inertial baseline by +14.43%, the UTD-MHAD skeleton baseline by +1.13% and achieves 96.11% on the Simitate motion capturing data (80/20 split). We further demonstrate experiments on both, modality fusion on a signal level and signal reduction to prevent the representation from overloading.

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Driver behavioural classification from trajectory data

Cited by 8 publications

References 6 publications

Driving behavior inference from traffic surveillance data

Driving behavior inference from traffic surveillance data

WhozDriving: Abnormal Driving Trajectory Detection by Studying Multi-faceted Driving Behavior Features

Gimme Signals: Discriminative signal encoding for multimodal activity recognition

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