Tempo-Spatial Compactness Based Background Subtraction for Vehicle Detection and Tracking
Background modelling techniques use the time, spatial, intensity and image plane information to detect the objects. These features are integrated to extract the maximum information. The utilization of background techniques are mostly dependent on various
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Abstract. Background modelling techniques use the time, spatial, intensity and image plane information to detect the objects. These features are integrated to extract the maximum information. The utilization of background techniques are mostly dependent on various parameters that can be learning rate or threshold. High dependency on parameters increase the complexity and make it difficult to control in changing weather conditions. Parameters based techniques do not provide the high efficiency in outdoor computer vision applications where illumination conditions are difficult to predict. This paper presents an algorithm that is based on background modelling with less dependency on parameters and robust to illumination changes. Camera jitter causes the major effect in modelling techniques so camera jitter is also addressed. A new way of separation of shadow from object is also implemented. Performance of the algorithm is compared with other state-of-the-art methods. Keywords: Background modelling
Illumination conditions Camera jitter
1 Introduction Vision-based surveillance has become a fast growing trend in recent times. A good surveillance system has the capability to inform any irregular behavior or movement that occurs on surveillance region. Extraction of information such as traffic flow, traffic density, speed detection and classification of vehicles for a traffic surveillance system is helpful for traffic management authorities to analyze the traffic. These approaches do not need any human assistance for continuous monitoring of video stream. Intelligent transportation systems use the vision and non-vision based sensors for traffic monitoring. Computer vision-based techniques are more popular when compared to the other traditional sensors due to its versatile features [1, 2]. Apparatus of these processes do not require pavement modification of highways during installations. Multiple detection zones and lanes are monitored at the same time. It links information that is gathered from different locations to turn into a wide area surveillance. Vision-based systems have been successfully used in many research applications [3–12]. Sensors of camera and computational power have been improved a lot that results in high reliability and robust performance of the systems [13]. Many techniques are proposed in © Springer International Publishing Switzerland 2016 D.-S. Huang et al. (Eds.): ICIC 2016, Part I, LNCS 9771, pp. 86–96, 2016. DOI: 10.1007/978-3-319-42291-6_9
Tempo-Spatial Compactness Based Background Subtraction
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literature for detection and segmentation of moving objects, such as inter-frame differencing, edge detection, optical flow, thresholding and background subtraction. [14, 15] present the detailed reviews of vision based methodologies for traffic surveillance. Background modelling approach is widely considered in the computer vision systems for traffic surveillance applications [16–19]. The compelling reason to select the background modelling approach in vision based surveillance is that the road conditions
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