A mathematical morphology based approach for vehicle detection in road tunnels

A novel approach to automatically detect vehicles in road tunnels is presented in this paper. Non-uniform and poor illumination conditions prevail in road tunnels making difficult to achieve robust vehicle detection. In order to cope with the illumination issues, we propose a local higher-order statistic filter to make the vehicle detection invariant to illumination changes, whereas a morphological-based background subtraction is used to generate a convex hull segmentation of the vehicles. An evaluation test comparing our approach with a benchmark object detector shows that our approach outperforms in terms of false detection rate and overlap area detection

Content-aware objective video quality assessment

Since the end-user of video-based systems is often a human observer, prediction of user-perceived video quality (PVQ) is an important task for increasing the user satisfaction. Despite the large variety of objective video quality measures (VQMs), their lack of generalizability remains a problem. This is mainly due to the strong dependency between PVQ and video content. Although this problem is well known, few existing VQMs directly account for the influence of video content on PVQ. Recently, we proposed a method to predict PVQ by introducing relevant video content features in the computation of video distortion measures. The method is based on analyzing the level of spatiotemporal activity in the video and using those as parameters of the anthropomorphic video distortion models. We focus on the experimental evaluation of the proposed methodology based on a total of five public databases, four different objective VQMs, and 105 content related indexes. Additionally, relying on the proposed method, we introduce an approach for selecting the levels of video distortions for the purpose of subjective quality assessment studies. Our results suggest that when adequately combined with content related indexes, even very simple distortion measures (e.g., peak signal to noise ratio) are able to achieve high performance, i.e., high correlation between the VQM and the PVQ. In particular, we have found that by incorporating video content features, it is possible to increase the performance of the VQM by up to 20% relative to its noncontent-aware baseline

Demo: real-time indoors people tracking in scalable camera networks

In this demo we present a people tracker in indoor environments. The tracker executes in a network of smart cameras with overlapping views. Special attention is given to real-time processing by distribution of tasks between the cameras and the fusion server. Each camera performs tasks of processing the images and tracking of people in the image plane. Instead of camera images, only metadata (a bounding box per person) are sent from each camera to the fusion server. The metadata are used on the server side to estimate the position of each person in real-world coordinates. Although the tracker is designed to suit any indoor environment, in this demo the tracker's performance is presented in a meeting scenario, where occlusions of people by other people and/or furniture are significant and occur frequently. Multiple cameras insure views from multiple angles, which keeps tracking accurate even in cases of severe occlusions in some of the views

Real-time vehicle matching for multi-camera tunnel surveillance

Tracking multiple vehicles with multiple cameras is a challenging problem of great importance in tunnel surveillance. One of the main challenges is accurate vehicle matching across the cameras with non-overlapping fields of view. Since systems dedicated to this task can contain hundreds of cameras which observe dozens of vehicles each, for a real-time performance computational efficiency is essential. In this paper, we propose a low complexity, yet highly accurate method for vehicle matching using vehicle signatures composed of Radon transform like projection profiles of the vehicle image. The proposed signatures can be calculated by a simple scan-line algorithm, by the camera software itself and transmitted to the central server or to the other cameras in a smart camera environment. The amount of data is drastically reduced compared to the whole image, which relaxes the data link capacity requirements. Experiments on real vehicle images, extracted from video sequences recorded in a tunnel by two distant security cameras, validate our approach

PhD forum: correlation coefficient based template matching for indoor people tracking

Abstract—One of the most popular methods to extract information from an image sequence is template matching. The principle of template matching is tracking a certain feature or target over time based on the comparison of the content of each frame with a simple template. In this article, we propose an correlation coefficient based template matching which is invariant to linear intensity distortions to do correction or verification of our existing indoor people tracking system

Non-overlapping multi-camera detection and tracking of vehicles in tunnel surveillance

We propose a real-time multi-camera tracking approach to follow vehicles in a tunnel surveillance environment with multiple non-overlapping cameras. In such system, vehicles have to be tracked in each camera and passed correctly from one camera to another through the tunnel. This task becomes extremely difficult when intra-camera errors are accumulated. Most typical issues to solve in tunnel scenes are due to low image quality, poor illumination and lighting from the vehicles. Vehicle detection is performed using Adaboost detector, speeded up by separating different cascades for cars and trucks improving general accuracy of detection. A Kalman Filter with two observations, given by the vehicle detector and an averaged optical flow vector, is used for single-camera tracking. Information from collected tracks is used for feeding the inter-camera matching algorithm, which measures the correlation of Radon transform-like projections between the vehicle images. Our main contribution is a novel method to reduce the false positive rate induced by the detection stage. We impose recall over precision in the detection correctness, and identify false positives patterns which are then included subsequently in a high-level decision making step. Results are presented for the case of 3 cameras placed consecutively in an inter-city tunnel. We demonstrate the increased tracking performance of our method compared to existing Bayesian filtering techniques for vehicle tracking in tunnel surveillance

EPSAC-controlled anesthesia with online gain adaptation

This paper presents the application of predictive control to drug dosing during anesthesia in patients undergoing surgery. A single-input (propofol) single-output (bispectral index (BIS)) model of the patient has been assumed for prediction. The performance of our previous strategy in drug dosing control has been improved to tackle inter-patient variability. A set of 12 patient models was studied and, in order to ensure the applicability of the proposed controller, gain adaptation in the controller is proposed. Preliminary studies have shown that due to static nonlinearity in the sigmoid curve of the patient model, feedback control is not feasible in the first part of the induction phase. Therefore, the control strategy applied in this study consists of controlling the effect site concentration (C-e) during the first phase and controlling BIS once the relation BIS/C-e has been identified. The policy of switching and adapting the control strategies shows a good performance during the induction phase in simulation studies. Clinical tests have been scheduled at the Ghent University Hospital for the coming months. Copyright (C) 2008 John Wiley & Sons, Ltd

PhD Forum: illumination-robust foreground detection for multi-camera occupancy mapping

Foreground detection is an essential preprocessing step for many image processing applications such as object tracking, human action recognition, pose estimation and occupancy mapping. Many existing techniques only perform well under steady illumination. Some approaches have been introduced to detect foreground under varying or sudden changes in illumination but the problem remains challenging. In this paper, we introduce a new texture-based foreground detection method which is robust to illumination change. Our method detects foreground by finding the correlation between the current frame and a background model. A region with low correlation is detected as foreground. We compare the performance of our proposed technique with other techniques from literature (edge-based, ViBe and Gaussian mixture model) as a preprocessing step of the multi-camera occupancy mapping system. The evaluation demonstrates that our technique outperforms the other methods in term of object loss