Robust segmentation for outdoor traffic surveillance

Monteiro, Gonçalo; Marcos, João; Ribeiro, M.; Batista, Jorge

doi:10.1109/icip.2008.4712339

Cited by 17 publications

(11 citation statements)

References 5 publications

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“…The process used to estimate the segmented foreground is not exploited in this paper, please refer to [17]. The main goal of this classifier is not to detect shadows accurately, but to filter out some impossible shadow pixels.…”

Section: Weak Shadow Classifiermentioning

confidence: 99%

Learning moving cast shadows for robust foreground detection in highway scenarios

Caseiro

Batista

2010

13th International IEEE Conference on Intelligent Transportation Systems

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In this work we present a method to automatically learn and detect cast shadows on highway surveillance scenarios. The first stage of this method uses a weak classifieras a pre-filter to select possible shadowed pixels in order to learn multi-layered statistical shadow models using a recursive Bayesian learning approach. These models will then be used, by a strong classifier, to correctly distinguish shadows. To prevent misclassifications from corrupting the results of both classifiers, spatial and temporal dependencies are also taken into account. Based on a Conditional Random Field (CRF), spatial/temporal dependencies in traffic scenes are formulated under a probabilistic discriminative framework, where contextual constraints during the detection process can be adaptively adjusted in terms of data-dependent neighborhood interaction. To meet real-time requirements the CRF energy function is minimized using the Dynamic Graph Cut algorithm (DGC) for the st-mincut/maxflow problem. This fast and fully dynamic algorithm uses the solution of the previous graph cut computation for solving the new instance of the problem. Our solution is data-driven and recursively adapts the shadow models to the changing conditions. The accuracy improves as it processes frames that contain shadows and is not limited by preset values. This technique is being used in a real outdoor traffic surveillance system in order to minimize the effects of cast vehicle shadows. Experimental results demonstrate the effectiveness of the proposed framework and the advantages of applying spatial contextualization and temporal coherence to the weak/strong classifiers.

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Section: Weak Shadow Classifiermentioning

confidence: 99%

Learning moving cast shadows for robust foreground detection in highway scenarios

Caseiro

Batista

2010

13th International IEEE Conference on Intelligent Transportation Systems

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“…In [9], they adopt the idea from and propose a method based on shadow confidence score (SCS) to separate vehicles and cast shadows from the foreground objects in traffic monitoring system .While it suggested that shadow removal method based on model is only applied to some special scenes with large and complex computations [10], so our proposed technique to match real-time requirement includes the shadow removal method based on properties of colour information like chromaticity as also proposed in [11,12] which proven to be noncomputationally expensive.…”

Section: Previous Workmentioning

confidence: 99%

Foreground segmentation-based human detection with shadow removal

Hafiz

Shafie

Khalifa

et al. 2010

International Conference on Computer and Communication Engineering (ICCCE'10)

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Recent research in video surveillance system has shown an increasing focus on creating reliable systems utilizing non-computationally expensive technique for observing humans' appearance, movements and activities, thus providing analytical information for advanced human behaviour analysis and realistic human modelling. In order for the system to function, it requires robust method for detecting human form from a given input of video streams. In this paper, we present a human detection technique suitable for video surveillance. The technique we propose includes background subtraction, foreground segmentation, and shadow removal. The proposed detection technique will first try to extract all foreground objects from the background and then moving shadows will be eliminated by a shadow detection algorithm. Finally, we perform a morphological reconstruction algorithm to recover the distorted foreground objects after shadow removal process. We define certain features that describe human and match them with the final objects obtained from earlier processing. The experimental result proves its validity and accuracy in various fixed outdoor and indoor video scenes.

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“…With advancement in image processing and computer vision techniques, much of the research has been done in the field of moving object's regions of change detection among multiple captured image sequences [13][14][15][16]. We categorise the vehicle detection and ð -Þ ð t Þ ¼ X segmentation techniques based on the approach used in each technique into three methods which are Background Subtraction; Feature Extraction-based and Motion-based.…”

Section: Vehicle Detection and Segmentation Techniquesmentioning

confidence: 99%

Performance Analysis of Vehicle Detection Techniques: A Concise Survey

Hanif¹,

Mansoor

Imran³

2018

Advances in Intelligent Systems and Computing

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Attention towards Intelligent Transportation System (ITS) has increased manifold especially due to prevailing security situation in the past decade. An integral part of ITS is video-based surveillance systems extracting real-time traffic parameters such as vehicle counting, vehicle classification, vehicle velocity etc. using stationary cameras installed on road sides. In all these systems, robust and reliable detection of vehicles is significantly a critical step. Since, several vehicle detection techniques exist, evaluating these techniques with respect to different environment conditions and application scenarios will give a better choice for actual deployment. The paper presents a concise survey of vehicle detection techniques used in diverse applications of video-based surveillance systems. Moreover, three main detection algorithms; Gaussian Mixture Model (GMM), Histogram of Gradients (HoG), and Adaptive motion Histograms based vehicle detection are implemented and evaluated for performance under varying illumination, traffic density and occlusion conditions. The survey provides a ready-reference for preferred vehicle detection technique under different applications.

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Robust segmentation for outdoor traffic surveillance

Cited by 17 publications

References 5 publications

Learning moving cast shadows for robust foreground detection in highway scenarios

Learning moving cast shadows for robust foreground detection in highway scenarios

Foreground segmentation-based human detection with shadow removal

Performance Analysis of Vehicle Detection Techniques: A Concise Survey

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