Shadow detection is critical for robust and reliable vision-based systems for traffic vision analysis. Shadow points are often misclassified as object points causing errors in localization, segmentation, tracking and classification of moving vehicles. This paper proposes a novel shadow elimination method SEBG for resolving shadow occlusion problems of vehicle analysis. Different from some traditional method which only consider intensity properties, this method introduces gradient feature to eliminate shadows. In this approach, moving foregrounds are first segmented from background by using a background subtraction technique. For all moving pixels, the approach SEBG using gradient feature to detect shadow pixels is presented in detail. This method is based on the observation that shadow regions present same textural characteristics in each frame of the video as in the corresponding adaptive background model. Gradient feature is robust to illumination changes. The method also needs no predefined parameters, which can well adapt to other video scene. Results validate the algorithm’s good performance on traffic video. 1.

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We are interested in designing a reusable and robust activity monitoring platform. In this paper we propose three good properties that an activity monitoring platform should have to enable its reusability for different applications and to insure performance quality: 1) modularity and flexibility of the architecture, 2) separation between the algorithms and the a priori knowledge they use, and 3) automatic evaluation of algorithm results. We then propose a development methodology to fulfill the last two properties. The methodology consists in the interaction between end-users and developers during the whole development of a specific monitoring system for a new application. To validate our approach, first we present a platform that we use to generate activity monitoring systems dedicated to specific applications. Then we explain how we have managed to give concrete expression to these properties in the platform. Finally we describe in details the technical validation and the end-user assessment of an automatic metro monitoring system built with the platform. We give the corresponding validation results for two other systems built with the same platform: a bank agency monitoring system and a building lock chamber access control system.

Abstract—In this paper, we propose novel methods to evaluate the performance of object detection algorithms in video sequences. This procedure allows us to highlight characteristics (e.g., region splitting or merging) which are specific of the method being used. The proposed framework compares the output of the algorithm with the ground truth and measures the differences according to objective metrics. In this way it is possible to perform a fair comparison among different methods, evaluating their strengths and weaknesses and allowing the user to perform a reliable choice of the best method for a specific application. We apply this methodology to segmentation algorithms recently proposed and describe their performance. These methods were evaluated in order to assess how well they can detect moving regions in an outdoor scene in fixed-camera situations. Index Terms—Ground truth, metrics, multiple interpretations, performance evaluation, segmentation, surveillance systems. I.

Identifying moving objects in a video sequence is a fundamental and critical task in many computer-vision applications. Background subtraction techniques are commonly used to separate foreground moving objects from the background. Most background subtraction techniques assume a single rate of adaptation, which is inadequate for complex scenes such as a traffic intersection where objects are moving at different and varying speeds. In this paper, we propose a foreground validation algorithm that first builds a foreground mask using a slow-adapting Kalman filter, and then validates individual foreground pixels by a simple moving object model, built using both the foreground and background statistics as well as the frame difference. Ground-truth experiments with urban traffic sequences show that our proposed algorithm significantly improves upon results using only Kalman filter or frame-differencing, and outperforms other techniques based on mixture of Gaussians, median filter, and approximated median filter.

A spatiotemporal saliency algorithm based on a center-surround framework is proposed. The algorithm is inspired by biological mechanisms of motion-based perceptual grouping and extends a discriminant formulation of center-surround saliency previously proposed for static imagery. Under this formulation, the saliency of a location is equated to the power of a predefined set of features to discriminate between the visual stimuli in a center and a surround window, centered at that location. The features are spatiotemporal video patches and are modeled as dynamic textures, to achieve a principled joint characterization of the spatial and temporal components of saliency. The combination of discriminant center-surround saliency with the modeling power of dynamic textures yields a robust, versatile, and fully unsupervised spatiotemporal saliency algorithm, applicable to scenes with highly dynamic backgrounds and moving cameras. The related problem of background subtraction is treated as the complement of saliency detection, by classifying nonsalient (with respect to appearance and motion dynamics) points in the visual field as background. The algorithm is tested for background subtraction on challenging sequences, and shown to substantially outperform various state-of-the-art techniques. Quantitatively, its average error rate is almost half that of the closest competitor.

In this paper we propose an approach to indoor environment surveillance and, in particular, to people behaviour control in home automation context. The reference application is a silent and automatic control of the behaviour of people living alone in the house and specially conceived for people with limited autonomy (e.g., elders or disabled people). The aim is to detect dangerous events (such as a person falling down) and to react to these events by establishing a remote connection with low-performance clients, such as PDA (Personal Digital Assistant). To this aim, we propose an integrated server architecture, typically connected in intranet with network cameras, able to segment and track objects of interest; in the case of objects classified as people, the system must also evaluate the people posture and infer possible dangerous situations. Finally, the system is equipped with a specifically designed transcoding server to adapt the video content to PDA requirements (display area and bandwidth) and to the user’s requests. The main issues of the proposal are a reliable real-time object detector and tracking module, a simple but effective posture classifier improved by a supervised learning phase, and an high performance transcoding inspired on MPEG-4 object-level standard, tailored to PDA. Results on different video sequences and performance analysis are discussed.

In this paper, we describe an integrated solution devised for In-House Video Surveillance, to control the safety of people living in a domestic environment. The system is composed of a robust moving object detection module, able to disregard shadows, a tracking module designed for large occlusion solution and of a posture detector. Shadows, large occlusions and deformable model of people are key features of in-house surveillance. Moreover, the requirements of high speed reaction to dangerous situations and the need to implement a reliable and low cost tele-viewing system, led to the introduction of a new multimedia model of semantic transcoding, capable to support different user’s requests and constraints of their devices (PDA, smart phones,…). Our application context is the emerging area of Domotics (from the Latin word domus that means “home ” and informatics) and, in particular, indoor video surveillance of the house where people with some difficulties (elders and disabled people) can now live in a sufficient degree of autonomy, thanks to the strong interaction with the new technologies that can be distributed in the house with affordable costs and high reliability. 1

Moving objects tracking is an important problem in many applications such as video-surveillance. Monitoring systems can be improved using vision-based techniques able to extract and classify objects in the scene. However, problems arise due to unexpected shadows because shadow detection is critical for accurate objects detection in video stream, since shadow points are often misclassified as object points causing errors in localization, segmentation, measurements, tracking and classification of moving objects. The paper presents a new approach for removing shadows from moving objects, starting from a frame-difference method using a gray-level textured adaptive background. The shadow detection scheme uses photometric properties and the notion of shadow as semi-transparent region which retains a reduced-contrast representation of the underlying surface pattern and texture. We analyze the problem of representing texture information in terms of redundant systems of functions for texture identification. The method for discriminating shadows from moving objects is based on a Pursuit scheme using an over-complete dictionary. The basic idea is to use the simple but powerful Matching Pursuit algorithm (MP) for representing texture as linear combination of elements of a big set of functions. Particularly, MP selects the best little set of atoms of 2D Gabor dictionary for features selection representative of properties of the texture in the image. Experimental results validate the algorithm’s performance.

Abstract — Despite their popularity, occupancy grids cannot be directly applied to problems where the identity of the objects populating an environment needs to be taken into account (eg object tracking, scene interpretation, etc), in this cases it is necessary to postprocess the grid in order to extract object information. This paper approaches the problem by proposing a novel algorithm inspired on image segmentation techniques. The proposed approach works without prior knowledge about the number of objects to be detected and, at the same time, is very fast. This is possible thanks to the use of a novel Self Organizing Network (SON) coupled with a dynamic threshold. Our experimental results on both real and simulated data show that our approach is robust and able to operate at normal camera framerate.