Tracking-Based Non-Parametric Background-Foreground Classification in a Chromaticity-Gradient Space

This work presents a novel background-foreground classification technique based on adaptive non-parametric kernel estimation in a color-gradient space of components. By combining normalized color components with their gradients, shadows are efficiently suppressed from the results, while the luminance information in the moving objects is preserved. Moreover, a fast multi-region iterative tracking strategy applied over previously detected foreground regions allows to construct a robust foreground modeling, which combined with the background model increases noticeably the quality in the detections. The proposed strategy has been applied to different kind of sequences, obtaining satisfactory results in complex situations such as those given by dynamic backgrounds, illumination changes, shadows and multiple moving objects

Moving cast shadows detection methods for video surveillance applications

Moving cast shadows are a major concern in today’s performance from broad range of many vision-based surveillance applications because they highly difficult the object classification task. Several shadow detection methods have been reported in the literature during the last years. They are mainly divided into two domains. One usually works with static images, whereas the second one uses image sequences, namely video content. In spite of the fact that both cases can be analogously analyzed, there is a difference in the application field. The first case, shadow detection methods can be exploited in order to obtain additional geometric and semantic cues about shape and position of its casting object (’shape from shadows’) as well as the localization of the light source. While in the second one, the main purpose is usually change detection, scene matching or surveillance (usually in a background subtraction context). Shadows can in fact modify in a negative way the shape and color of the target object and therefore affect the performance of scene analysis and interpretation in many applications. This chapter wills mainly reviews shadow detection methods as well as their taxonomies related with the second case, thus aiming at those shadows which are associated with moving objects (moving shadows).Peer Reviewe

Detection And Tracking Of Moving Objects using Particle Filter

Motion detection is the first essential process in the extraction of information regarding moving objects and makes use of stabilization in functional areas such as tracking, classification, recognition, and so on. In this paper, high - quality moving object detection is determined by using nonparametric modeling. The background is modeled by using the combination of chromaticity and gradients; it reduces the influence of shadows and reflected light. The foreground model combines this information and spatial information. Particle filter is introduced update the spatial information. The detection results produced by the partic le filter is analysed through visual inspection and for accuracy, along with comparisons to the results produced by other state - of - the - art methods

A statistical approach for shadow detection using spatio-temporal contexts

Background subtraction is an important step used to segment moving regions in surveillance videos. However, cast shadows are often falsely labeled as foreground objects, which may severely degrade the accuracy of object localization and detection. Effective shadow detection is necessary for accurate foreground segmentation, especially for outdoor scenes. Based on the characteristics of shadows, such as luminance reduction, chromaticity consistency and texture consistency, we introduce a nonparametric framework for modeling surface behavior under cast shadows. To each pixel, we assign a potential shadow value with a confidence weight, indicating the probability that the pixel location is an actual shadow point. Given an observed RGB value for a pixel in a new frame, we use its recent spatio-temporal context to compute an expected shadow RGB value. The similarity between the observed and the expected shadow RGB values determines whether a pixel position is a true shadow. Experimental results show the performance of the proposed method on a suite of standard indoor and outdoor video sequences

Biologically-inspired robust motion segmentation using mutual information

This paper presents a neuroscience inspired information theoretic approach to motion segmentation. Robust motion segmentation represents a fundamental first stage in many surveillance tasks. As an alternative to widely adopted individual segmentation approaches, which are challenged in different ways by imagery exhibiting a wide range of environmental variation and irrelevant motion, this paper presents a new biologically-inspired approach which computes the multivariate mutual information between multiple complementary motion segmentation outputs. Performance evaluation across a range of datasets and against competing segmentation methods demonstrates robust performance

Detecting and Shadows in the HSV Color Space Using Dynamic Thresholds

The detection of moving objects in a video sequence is an essential step in almost all the systems of vision by computer. However, because of the dynamic change in natural scenes, the detection of movement becomes a more difficult task. In this work, we propose a new method for the detection moving objects that is robust to shadows, noise and illumination changes. For this purpose, the detection phase of the proposed method is an adaptation of the MOG approach where the foreground is extracted by considering the HSV color space. To allow the method not to take shadows into consideration during the detection process, we developed a new shade removal technique based on a dynamic thresholding of detected pixels of the foreground. The calculation model of the threshold is established by two statistical analysis tools that take into account the degree of the shadow in the scene and the robustness to noise.  Experiments undertaken on a set of video sequences showed that the method put forward provides better results compared to existing methods that are limited to using static thresholds

Detecting and Shadows in the HSV Color Space using Dynamic Thresholds

The detection of moving objects in a video sequence is an essential step in almost all the systems of vision by computer. However, because of the dynamic change in natural scenes, the detection of movement becomes a more difficult task. In this work, we propose a new method for the detection moving objects that is robust to shadows, noise and illumination changes. For this purpose, the detection phase of the proposed method is an adaptation of the MOG approach where the foreground is extracted by considering the HSV color space. To allow the method not to take shadows into consideration during the detection process, we developed a new shade removal technique based on a dynamic thresholding of detected pixels of the foreground. The calculation model of the threshold is established by two statistical analysis tools that take into account the degree of the shadow in the scene and the robustness to noise.  Experiments undertaken on a set of video sequences showed that the method put forward provides better results compared to existing methods that are limited to using static thresholds

A new strategy of detecting traffic information based on traffic camera : modified inverse perspective mapping

The development of Intelligent Transportation Systems (ITS) needs high quality traffic information such as intersections, but conventional image-based traffic detection methods have difficulties with perspective and background noise, shadows and lighting transitions. In this paper, we propose a new traffic information detection method based on Modified Inverse Perspective Mapping (MIPM) to perform under these challenging conditions. In our proposed method, first the perspective is removed from the images using the Modified Inverse Perspective Mapping (MIPM); afterward, Hough transform is applied to extract structural information like road lines and lanes; then, Gaussian Mixture Models are used to generate the binary image. Meanwhile, to tackle shadow effect in car areas, we have applied a chromacity-base strategy. To evaluate the performance of the proposed method, we used several video sequences as benchmarks. These videos are captured in normal weather from a high way, and contain different types of locations and occlusions between cars. Our simulation results indicate that the proposed algorithms and frameworks are effective, robust and more accurate compared to other frameworks, especially in facing different kinds of occlusions

Efficient Vehicle Counting and Classification using Robust Multi-Cue Consecutive Frame Subtraction

The ability to count and classify vehicles provides valuable information to road network managers, highways agencies and traffic operators alike, enabling them to manage traffic and to plan future development of the network. Increased computational speed of processors has enabled application of vision technology in several fields such as: Industrial automation, Video security, transportation and automotive. The proposed method in this paper is a robust adaptive multi-cue frame subtraction method that detects foreground pixels corresponding to moving and stopped vehicles, even with noisy images due to compression. First the approach adaptively thresholds a combination of luminance and chromaticity disparity maps between the learned background and the current frame. The segmentation is further used by a two-step tracking approach, which combines the simplicity of a linear 2-D Kalman filter and the complexity of 3-D volume estimation using Markov chain Monte Carlo (MCMC) methods. The experimental results shows that the proposed method can count and classify vehicles in real time with a high level of performance under challenging situations, such as with moving casted shadows on sunny days, headlight reflections on the road using only a single standard camera