Robust Traffic State Estimation on Smart Cameras

Pletzer, Felix; Tusch, Roland; Böszörményi, László; Rinner, Bernhard

doi:10.1109/avss.2012.63

Cited by 8 publications

(5 citation statements)

References 14 publications

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“…Therefore, the workload reflects the arrival rate of the vehicles. We used real workloads for the learning algorithm by taking several test recordings on different highways, where we recorded the interarrival times of vehicles by a vehicle detection algorithm [Pletzer et al 2012]. Figure 5 shows the interarrival times distribution (spaced by an interval of 5 seconds) of four different test recordings on different locations.…”

Section: Workload Estimationmentioning

confidence: 99%

“…The motivation of this research is based on our previous work on traffic monitoring [Pletzer et al 2012;Bischof et al 2010] and the development of a mobile, multi-camera traffic surveillance system [Khan et al 2011]. In contrast to most of the existing traffic surveillance systems which are mostly based on fixed installations and large sensors, our portable platform can be easily deployed and used for various monitoring tasks, including law enforcement and construction site monitoring.…”

Section: Introductionmentioning

confidence: 98%

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Online learning of timeout policies for dynamic power management

Khan

Rinner

2014

ACM Trans. Embed. Comput. Syst.

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Dynamic power management (DPM) refers to strategies which selectively change the operational states of a device during runtime to reduce the power consumption based on the past usage pattern, the current workload, and the given performance constraint. The power management problem becomes more challenging when the workload exhibits nonstationary behavior which may degrade the performance of any single or static DPM policy.This article presents a reinforcement learning (RL)-based DPM technique for optimal selection of timeout values in the different device states. Each timeout period determines how long the device will remain in a particular state before the transition decision is taken. The timeout selection is based on workload estimates derived from a Multilayer Artificial Neural Network (ML-ANN) and an objective function given by weighted performance and power parameters. Our DPM approach is further able to adapt the powerperformance weights online to meet user-specified power and performance constraints, respectively. We have completely implemented our DPM algorithm on our embedded traffic surveillance platform and performed long-term experiments using real traffic data to demonstrate the effectiveness of the DPM. Our results show that the proposed learning algorithm not only adequately explores the power-performance trade-off with nonstationary workload but can also successfully perform online adjustment of the trade-off parameter in order to meet the user-specified constraint. ACM Reference Format: U. A. Khan and B. Rinner. 2014. Online learning of timeout policies for dynamic power management. ACM Trans. Embedd. Comput.

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Section: Workload Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Online learning of timeout policies for dynamic power management

Khan

Rinner

2014

ACM Trans. Embed. Comput. Syst.

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“…The last factor requires an extended comment. Some approaches described in the literature [41,52] were designed and tested on sequences recorded by cameras mounted over a road, where the vehicle movement is usually smooth (e.g. highway).…”

Section: Vehicle Detection and Countingmentioning

confidence: 99%

Real-time hardware–software embedded vision system for ITS smart camera implemented in Zynq SoC

Kryjak

Komorkiewicz

Gorgon

2016

J Real-Time Image Proc

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The article demonstrates the usefulness of heterogeneous System on Chip (SoC) devices in smart cameras used in intelligent transportation systems (ITS). In a compact, energy efficient system the following exemplary algorithms were implemented: vehicle queue length estimation, vehicle detection, vehicle counting and speed estimation (using multiple virtual detection lines), as well as vehicle type (local binary features and SVM classifier) and colour (k-means classifier and YCbCr colourspace analysis) recognition. The solution exploits the hardwaresoftware architecture, i.e. the combination of reconfigurable resources and the efficient ARM processor. Most of the modules were implemented in hardware, using Verilog HDL, taking full advantage of the possible parallelization and pipeline, which allowed to obtain real-time image processing. The ARM processor is responsible for executing some parts of the algorithm, i.e. high-level image processing and analysis, as well as for communication with the external systems (e.g. traffic lights controllers). The demonstrated results indicate that modern SoC systems are a very interesting platform for advanced ITS systems and other advanced embedded image processing, analysis and recognition applications.Keywords Intelligent Transportation Systems Á Hardware-software image processing (Zynq SoC) Á Vehicle queue length estimation Á Vehicle detection Á Vehicle type and colour recognition

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“…It would require expensive hardware to track objects using state-of-the-art tracking methods, such as particle filters [40]. [18] Edge detection Wolf et al [3] Human gesture recognition, region extraction, contour detection, and template matching Lin et al [23] Gesture recognition Muehlmann et al [50] Real-time tracking Heyrman et al [51] Motion detection Bramberger et al [9] Traffic surveillance, multicamera object tracking Chen and Aghajan [19] Gesture recognition using smart camera network Quaritsch et al [21] Multicamera tracking, camShift Rinner and Wolf [4] scene abstraction Aghajan et al [20] Human pose estimation Sankaranarayanan et al [24] Object detection, recognition, and tracking Tessens et al [30] Foreground detection, subsampling Wang et al [22] Tracking, event detection, and foreground detection Casares and Velipasalar [28] Foreground detection, tracking feedback Sidla et al [52] Traffic monitoring Pletzer et al [53] Traffic monitoring, vehicle speed, and vehicle count Wang et al [29] Foreground detection, contour tracking Cuevas and Garcia [54] Single camera tracking, background modelling In order to decide the best view, smart cameras need to share foreground information with each other. Figure 4 shows the fraction of image area that belongs to the foreground for real surveillance footage of 24 hours.…”

Section: Limitations Of Smart Camerasmentioning

confidence: 99%

From Smart Camera to SmartHub: Embracing Cloud for Video Surveillance

Saini

Atrey

Saddik

2014

International Journal of Distributed Sensor Networks

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Smart cameras were conceived to provide scalable solutions to automatic video analysis applications, such as surveillance and monitoring. Since then, many algorithms and system architectures have been proposed, which use smart cameras to distribute functionality and save bandwidth. Still, smart cameras are rarely used in commercial systems and real installations. In this paper, we investigate the reason behind the scarce commercial usage of smart cameras. We found that, in order to achieve scalability, smart cameras put additional constraints on the quality of input data to the vision algorithms, making it an unfavourable choice for future multicamera systems. We recognized that these constraints can be relaxed by following a cloud based hub architecture and propose a cloud entity, SmartHub, which provides a scalable solution with reduced constraints on the quality. A framework is proposed for designing SmartHub system for a given camera placement. Experiments show the efficacy of SmartHub based systems in multicamera scenarios.

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Robust Traffic State Estimation on Smart Cameras

Cited by 8 publications

References 14 publications

Online learning of timeout policies for dynamic power management

Online learning of timeout policies for dynamic power management

Real-time hardware–software embedded vision system for ITS smart camera implemented in Zynq SoC

From Smart Camera to SmartHub: Embracing Cloud for Video Surveillance

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