Real-time change detection for surveillance in public transportation

Alexandropoulos, T.; Boutas, S.; Loumos, V.; Kayafas, E.

doi:10.1109/avss.2005.1577243

Cited by 6 publications

(3 citation statements)

References 7 publications

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“…In order to achieve an accurate noise model approximation, it is sufficient to restrict the noise-model calculations to the block subset which exhibits noise-level fluctuations. Therefore, the goal of the proposed procedure is to group blocks of the absolute difference to clusters, according to their statistic similarity (Alexandropoulos, 2005).…”

Section: Noise Model Estimationmentioning

confidence: 99%

Change Detection and Background Update Through Statistic Segmentation for Traffic Monitoring

Alexandropoulos¹,

Loumos

Kayafas

2007

Proceedings of the Second International Conference on Signal Processing and Multimedia Applications

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Recent advances in computer imaging have led to the emergence of video-based surveillance as a monitoring solution in Intelligent Transportation Systems (ITS). The deployment of CCTV infrastructure in highway scenes facilitates the evaluation of traffic conditions. However, the majority of video-based ITS are restricted to manual assessment and lack the ability to support automatic event notification. This is due to the fact that, the effective operation of intelligent traffic management relies strongly on the performance of an image processing front end, which performs change detection and background update. Each one of these tasks needs to cope with specific challenges. Change detection is required to perform the effective isolation of content changes from noise-level fluctuations, while background update needs to adapt to timevarying lighting variations, without incorporating stationary occlusions to the background. This paper presents the operation principle of a video-based ITS front end. A block-based statistic segmentation method for feature extraction in highway scenes is analyzed. The presented segmentation algorithm focuses on the estimation of the noise model. The extracted noise model is utilized in change detection in order to separate content changes from noise fluctuations. Additionally, a statistic background estimation method, which adapts to gradual illumination variations, is presented.

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Section: Noise Model Estimationmentioning

confidence: 99%

Change Detection and Background Update Through Statistic Segmentation for Traffic Monitoring

Alexandropoulos¹,

Loumos

Kayafas

2007

Proceedings of the Second International Conference on Signal Processing and Multimedia Applications

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“…A task running on a hard real-time system must return response before its deadline to avoid any critical failure. Examples of hard real-time monitoring systems include medical-care systems (Khatib et al 2006), critical environmental monitoring systems (McIntire et al 2006;Alexandropoulos et al 2005), and driver-vigilance monitoring systems (Bergasa et al 2006). Many sensor devices consume more power and energy than a processor.…”

Section: Introductionmentioning

confidence: 99%

A DVS-assisted hard real-time I/O device scheduling algorithm

et al. 2009

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The I/O subsystem has become a major source of energy consumption in a hard real-time monitoring and control system. To reduce its energy consumption without missing deadlines, a dynamic power management (DPM) policy must carefully consider the power parameters of a device, such as its break-even time and wake-up latency, when switching off idle devices. This problem becomes extremely complicated when dynamic voltage scaling (DVS) is applied to change the execution time of a task. In this paper, we present COLORS, a composite low-power scheduling framework that includes DVS in a DPM policy to maximize the energy reduction on the I/O subsystem. COLORS dynamically predicts the earliest-access time of a device and switches off idle devices. It makes use of both static and dynamic slack time to extend the execution time of a task by DVS, in order to create additional switchoff opportunities. Task workloads, processor profiles, and device characteristics all impact the performance of a low-power real-time algorithm. We also identify a key Real-Time Syst (2009) 41: 222-255 223 metric that primarily determines its performance. The experimental results show that, compared with previous work, COLORS achieves additional energy reduction up to 20%, due to the efficient utilization of slack time.

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“…Vehicle tracking approaches can be classified by feature selection as follows: Region Based Tracking [5], [10], [13], Color Based Tracking [6], [11], [12] and Contour Based Tracking [7]- [9]. In Region Based Tracking, a background model is generated for an image.…”

Section: Introductionmentioning

confidence: 99%