Real-time vision-based multiple vehicle detection and tracking for nighttime traffic surveillance

Chen, Yen‐Lin; Wu, Bing-Fei; Fan, Chung-Jui

doi:10.1109/icsmc.2009.5346191

Cited by 36 publications

(16 citation statements)

References 11 publications

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“…For instance, in the presence of fog or rain, vehicle detection or counting is more challenging. For night-time scenarios, street lamps and vehicle lights also have to be considered [77]. In particular, street lamps, traffic lights can be used for vehicle sensing, as vehicles can be detected by considering the distance between vehicle's headlights.…”

Section: Applicationsmentioning

confidence: 99%

The Role of Advanced Sensing in Smart Cities

Hancke

Silva

2012

Sensors

508

271

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In a world where resources are scarce and urban areas consume the vast majority of these resources, it is vital to make cities greener and more sustainable. Advanced systems to improve and automate processes within a city will play a leading role in smart cities. From smart design of buildings, which capture rain water for later use, to intelligent control systems, which can monitor infrastructures autonomously, the possible improvements enabled by sensing technologies are immense. Ubiquitous sensing poses numerous challenges, which are of a technological or social nature. This paper presents an overview of the state of the art with regards to sensing in smart cities. Topics include sensing applications in smart cities, sensing platforms and technical challenges associated with these technologies. In an effort to provide a holistic view of how sensing technologies play a role in smart cities, a range of applications and technical challenges associated with these applications are discussed. As some of these applications and technologies belong to different disciplines, the material presented in this paper attempts to bridge these to provide a broad overview, which can be of help to researchers and developers in understanding how advanced sensing can play a role in smart cities.

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Section: Applicationsmentioning

confidence: 99%

The Role of Advanced Sensing in Smart Cities

Hancke

Silva

2012

Sensors

508

271

View full text Add to dashboard Cite

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“…Furthermore, under conditions of badillumination, the background scenes are substantially affected by lighting effect of moving vehicles, so that those obvious cues of background models which are effective for vehicle detection during daytime become invalid. Thus, most of the abovementioned frame-differencing based techniques may not work well under such Weather traffic environments [25].…”

Section: Background Subtraction Using Fuzzy Automatamentioning

confidence: 99%

Extension of Cellular Automata for Dynamic Vehicle Tracking

Darwish

2016

Int. J. ITS Res.

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Detecting and tracking moving vehicles in realtime traffic scenes are an emerging research area for intelligent transportation systems. This paper presents the computational model of Fuzzy Cellular Automata (FCA) to address the susceptible to environmental changes' problem associated with the background subtraction methods for dynamic vehicle tracking. The suggested model deploys FCA that is configured with rules supporting least sensitive fuzzy BExclusive orf unction as next state logic to handle degrees of uncertainty in rule similarly operations. Indeed, at each step, the update of background in frame difference approaches is determined according to the number of active cells and fuzzy mapping function; so moving vehicles that their gray level is entirely similar to the background gray level are simply detected. Furthermore, an occlusion handling technique based on visual measurement is employed to detect the classes of the vehicle occlusions and segment the vehicle from each occlusive class. The experimental results show that the suggested method is more robust, accurate and powerful than traditional methods, for real-time vehicle detection and tracking.

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“…If the light intensity is higher than or equal to a specific threshold, then the accordingly daytime algorithms are triggered and executed. On the other hand, if the light intensity is lower than the threshold, then the nighttime algorithms are used [2].…”

Section: Introductionmentioning

confidence: 99%

A Warning System for Obstacle Detection at Vehicle Lateral Blind Spot Area

Lee

Huang

2013

2013 7th Asia Modelling Symposium

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For a real driver assistance system, the weather, driving speed, and background could affect the accuracy of obstacle detection. In the past, only a few studies covered all the different weather conditions and almost none of them had paid attention to the safety at vehicle lateral blind spot area. So, this paper proposes a hybrid scheme for pedestrian and vehicle detection, and develop a warning system dedicated for lateral blind spot area under different weather conditions and driving speeds. More specifically, the HOG and SVM methods are used for pedestrian detection. The image subtraction, edge detection and tire detection are applied for vehicle detection. Experimental results also show that the proposed system can efficiently detect pedestrian and vehicle under several scenarios.

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Real-time vision-based multiple vehicle detection and tracking for nighttime traffic surveillance

Cited by 36 publications

References 11 publications

The Role of Advanced Sensing in Smart Cities

The Role of Advanced Sensing in Smart Cities

Extension of Cellular Automata for Dynamic Vehicle Tracking

A Warning System for Obstacle Detection at Vehicle Lateral Blind Spot Area

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