Real-Time Video-Based Traffic Measurement and Visualization System for Energy/Emissions

Morris, Brendan; Tran, Cuong; Scora, George; Trivedi, Mohan M.; Barth, Matthew

doi:10.1109/tits.2012.2208222

Cited by 63 publications

(22 citation statements)

References 26 publications

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“…3) Higher level transportation analysis can be built on top of basic vehicle monitoring. As an example, transportation environmental pollution impacts can be assessed along the road network by utilizing emission models for specific vehicles along with vehicle tracking and type classification [253]. In addition, regular patterns of traffic flow and traffic jams can be discovered with the vehicle trajectory analysis.…”

Section: Discussion Of Future Developmentsmentioning

confidence: 99%

“…For example, accurate recognition of vehicle types could offer valuable assistance to the police in identifying blacklisted vehicles from a mass of traffic surveillance image database [97] or could be used for emission estimation [133]. The researches of vehicle classification methods are mainly based on the following two aspects: shape features and appearances of vehicles.…”

Section: Vehicle Recognitionmentioning

confidence: 99%

See 1 more Smart Citation

Hierarchical and Networked Vehicle Surveillance in ITS: A Survey

Tian

Morris

Tang

et al. 2017

IEEE Trans. Intell. Transport. Syst.

Self Cite

108

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Traffic surveillance has become an important topic in intelligent transportation systems (ITSs), which is aimed at monitoring and managing traffic flow. With the progress in computer vision, video-based surveillance systems have made great advances on traffic surveillance in ITSs. However, the performance of most existing surveillance systems is susceptible to challenging complex traffic scenes (e.g., object occlusion, pose variation, and cluttered background). Moreover, existing related research is mainly on a single video sensor node, which is incapable of addressing the surveillance of traffic road networks. Accordingly, we present a review of the literature on the video-based vehicle surveillance systems in ITSs. We analyze the existing challenges in video-based surveillance systems for the vehicle and present a general architecture for video surveillance systems, i.e., the hierarchical and networked vehicle surveillance, to survey the different existing and potential techniques. Then, different methods are reviewed and discussed with respect to each module. Applications and future developments are discussed to provide future needs of ITS services.

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Section: Discussion Of Future Developmentsmentioning

confidence: 99%

Section: Vehicle Recognitionmentioning

confidence: 99%

Hierarchical and Networked Vehicle Surveillance in ITS: A Survey

Tian

Morris

Tang

et al. 2017

IEEE Trans. Intell. Transport. Syst.

Self Cite

108

View full text Add to dashboard Cite

show abstract

“…Well-developed glyph-based [31] visualization technique is proposed that can enable effective and efficient visual communication and information encoding. Morris [32] method only visualize the traffic information using color-coded scheme which is unable to represent whether the vehicle change the lane or not. Whereas the proposed system visualization method is able to visualize the traffic information as well as vehicle lane change information.…”

Section: Introductionmentioning

confidence: 99%

Glyph-based video visualization on Google Map for surveillance in smart cities

Mehboob

Abbas

Rehman

et al. 2017

J Image Video Proc.

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Video visualization (VV) is considered to be an essential part of multimedia visual analytics. Many challenges have arisen from the enormous video content of cameras which can be solved with the help of data analytics and hence gaining importance. However, the rapid advancement of digital technologies has resulted in an explosion of video data, which stimulates the needs for creating computer graphics and visualization from videos. Particularly, in the paradigm of smart cities, video surveillance as a widely applied technology can generate huge amount of videos from 24/7 surveillance. In this paper, a state of the art algorithm has been proposed for 3D conversion from traffic video content to Google Map. Time-stamped glyph-based visualization is used effectively in outdoor surveillance videos and can be used for event-aware detection. This form of traffic visualization can potentially reduce the data complexity, having holistic view from larger collection of videos. The efficacy of the proposed scheme has been shown by acquiring several unprocessed surveillance videos and by testing our algorithm on them without their pertaining field conditions. Experimental results show that the proposed visualization technique produces promising results and found effective in conveying meaningful information while alleviating the need of searching exhaustively colossal amount of video data.

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“…It plays a key role in applications like surveillance analysis [1,2] and traffic monitoring [3,4]. Decades of research have led to tremendous progress in this field.…”

Section: Introductionmentioning

confidence: 99%

Robust tracking-by-detection using a selection and completion mechanism

Fan

Zhang

et al. 2017

Comp. Visual Media

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It is challenging to track a target continuously in videos with long-term occlusion, or objects which leave then re-enter a scene. Existing tracking algorithms combined with onlinetrained object detectors perform unreliably in complex conditions, and can only provide discontinuous trajectories with jumps in position when the object is occluded. This paper proposes a novel framework of tracking-by-detection using selection and completion to solve the abovementioned problems. It has two components, tracking and trajectory completion. An offline-trained object detector can localize objects in the same category as the object being tracked. The object detector is based on a highly accurate deep learning model. The object selector determines which object should be used to re-initialize a traditional tracker. As the object selector is trained online, it allows the framework to be adaptable. During completion, a predictive non-linear autoregressive neural network completes any discontinuous trajectory. The tracking component is an online real-time algorithm, and the completion part is an after-theevent mechanism. Quantitative experiments show a significant improvement in robustness over prior stateof-the-art methods.

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Real-Time Video-Based Traffic Measurement and Visualization System for Energy/Emissions

Cited by 63 publications

References 26 publications

Hierarchical and Networked Vehicle Surveillance in ITS: A Survey

Hierarchical and Networked Vehicle Surveillance in ITS: A Survey

Glyph-based video visualization on Google Map for surveillance in smart cities

Robust tracking-by-detection using a selection and completion mechanism

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