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

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

PhD forum: multi-view occupancy maps using a network of low resolution visual sensors

An occupancy map provides an abstract top view of a scene and can be used for many applications such as domotics, surveillance, elderly-care and video teleconferencing. Such maps can be accurately estimated from multiple camera views. However, using a network of regular high resolution cameras makes the system expensive, and quickly raises privacy concerns (e. g. in elderly homes). Furthermore, their power consumption makes battery operation difficult. A solution could be the use of a network of low resolution visual sensors, but their limited resolution could degrade the accuracy of the maps. In this paper we used simulations to determine the minimum required resolution needed for deriving accurate occupancy maps which were then used to track people. Multi-view occupancy maps were computed from foreground silhouettes derived via an analysis of moving edges. Ground occupancies computed from each view were fused in a Dempster-Shafer framework. Tracking was done via a Bayes filter using the occupancy map per time instance as measurement. We found that for a room of 8.8 by 9.2 m, 4 cameras with a resolution as low as 64 by 48 pixels was sufficient to estimate accurate occupancy maps and track up to 4 people. These findings indicate that it is possible to use low resolution visual sensors to build a cheap, power efficient and privacy-friendly system for occupancy monitoring

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

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

Image projection clues for improved real-time vehicle tracking in tunnels

Vehicle tracking is of great importance for tunnel safety. To detect incidents or disturbances in traffic flow it is necessary to reliably track vehicles in real-time. The tracking is a challenging task due to poor lighting conditions in tunnels and frequent light reflections from tunnel walls, the road and the vehicles themselves. In this paper we propose a multi-clue tracking approach combining foreground blobs, optical flow of Shi-Tomasi features and image projection profiles in a Kalman filter with a constant velocity model. The main novelty of our approach lies in using vertical and horizontal image projection profiles (so-called vehicle signatures) as additional measurements to overcome the problems of inconsistent foreground and optical flow clues in cases of severe lighting changes. These signatures consist of Radon-transform like projections along each image column and row. We compare the signatures from two successive video frames to find their alignment and to correct predicted vehicle position and size. We tested our approach on several tunnel sequences. The results show an improvement in the accuracy of the tracker and less target losses when image projection clues are used. Furthermore, calculation and comparison of image projections is computationally efficient so the tracker keeps real-time performance (25 fps, on a single 1.86 GHz processor)

Evaluation of background/foreground segmentation methods for multi-view occupancy maps

An occupancy map provides a top view of a scene and can be used for monitoring the activity of people. We estimate occupancy maps using foreground silhouettes from multiple camera views. The ground occupancies computed from each view are fused in a Dempster-Shafer framework. However, it is not clear which background/foreground segmentation method for deriving the silhouettes is most suited for estimating our occupancy maps. We evaluated three segmentation methods from literature (ViBe, gaussian mixture model, method by Petrovic et al.), and one new segmentation method based on the analysis of edges. Occupancy maps were calculated for the APIDIS dataset, and the obtained maps were evaluated using the players' ground truth positions. We found that all methods perform similar in terms of the accuracy of the estimated maps, except the edges based segmentation method which outperforms all other methods. Future work will include texture based segmentation methods, and will focus on robustness with regard to lighting changes