Pedro Mendes Jorge

Tracking Groups of Pedestrians in Video Sequences

This paper describes an algorithm for tracking groups of objects in video sequences. The main difficulties addressed in this work concern total occlusions of the objects to be tracked as well as group merging and splitting. A two layer solution is proposed to overcome these difficulties. The first layer produces a set of spatio temporal strokes based on low level operations which manage to track the active regions most of the time. The second layer performs a consistent labeling of the detected segments using a statistical model based on Bayesian networks. The Bayesian network is recursively computed during the tracking operation and allows the update of the tracker results everytime new information is available. Experimental tests are included to show the performance of the algorithm in ambiguous situations.

Hidden Markov models vs. syntactic modeling in object recognition

This paper addresses the problem of object recognition based on contour descriptions. Two approaches, namely hidden Markov models (HMM) and syntactic modeling based on stochastic finite-state grammars (SFSG), are analyzed and applied to the classification of hardware tools. It is shown that both approaches are able to capture the data variability, leading to high classification performance. While the syntactic paradigm is flexible, the structure of the grammars being automatically inferred from the data, the HMMs are more robust in terms of training data sets requirements.

Visual inspection of a combustion process in a thermoelectric plant

Abstract Infrared images provide useful information to inspect the status of combustion processes. The flame geometry and intensity depend on the combustion status and can be used for control and monitoring purposes. Flame segmentation is difficult since the background intensity is sometimes higher than the flame intensity, therefore requiring the use of sophisticated image analysis algorithms. This paper describes methods to analyze infrared images of industrial flames and to characterize the flame geometry. A segmentation algorithm is proposed to separate the flame region from the background using an image formation model, a background model and the available shape information. Segmentation algorithms (e.g., active contours) usually assume solid objects with sharp boundaries. This is not true in the case of flame images. The flame is nonhomogeneous and it has a fuzzy boundary. To circumvent this difficulty multiple contours are used to characterize the flame geometry. The flame shape is then obtained by robust estimation methods, using a model of the image formation process inside the combustion chamber. The proposed algorithm is evaluated and used to monitor the flame characteristics in a boiler of a thermoelectric plant.

Automatic Vehicle Detection and Classification

This paper presents a proposal for an automatic vehicle detection and classification (AVDC) system. The proposed AVDC should classify vehicles accordingly to the Portuguese legislation (vehicle height over the first axel and number of axels), and should also support profile based classification. The AVDC should also fulfill the needs of the Portuguese motorway operator, Brisa. For the classification based on the profile we propose the use of Eigenprofiles, a technique based on Principal Components Analysis. The system should also support multi-lane free flow for future integration in this kind of environments.

Estimation of the Bayesian network architecture for object tracking in video sequences

It was recently proposed the use of Bayesian networks for object tracking. Bayesian networks allow modeling the interaction among detected trajectories, in order to obtain reliable object identification in the presence of occlusions. However, the architecture of the Bayesian network has been defined using simple heuristic rules, which fail in many cases. This paper addresses the above problem and presents a new method to estimate the network architecture from the video sequences using supervised learning techniques. Experimental results are presented showing that significant performance gains (increase of accuracy and decrease of complexity) are achieved by the proposed methods.

Tracking with Bayesian networks: extension to arbitrary topologies

It was recently proposed an object tracking method, which is able to deal with object occlusions and group tracking, using Bayesian networks. The Bayesian network (BN) tracker has shown promising results in difficult situations but its architecture is limited to a maximum of 2 parents/2 children per node, in order to avoid the combinatorial explosion and difficult network generation procedures from the video signal. This paper addresses the major limitation of the BN tracker and presents a method to generalize the tracker to cope with arbitrary topologies, allowing the tracker to operate in more complex scenes.

Evaluation of an LPR-Based Toll Enforcement System on Portuguese Motorways

This paper presents an approach for evaluating video-based enforcement systems for motorway toll collection, which has been applied to the case of Portugals largest motorway operator, Brisa. The results of this evaluation have contributed to the design of a new LPR system, denoted advanced license plate recognition (ALPR), also described in this paper. The ALPR is currently being deployed not only by Brisa, but also by other operators that use the Via Verde system. A significant decrease in the need for human intervention has been an important improvement, in which the introduction of a tunable and trustable confidence level in the LPR process has played a key part

Integrated vehicle classification system

This paper presents an integrated system for vehicle classification. This system aims to classify vehicles using different approaches: 1) based on the height of the first axle and_the number of axles; 2) based on volumetric measurements and; 3) based on features extracted from the captured image of the vehicle. The system uses a laser sensor for measurements and a set of image analysis algorithms to compute some visual features. By combining different classification methods, it is shown that the system improves its accuracy and robustness, enabling its usage in more difficult environments satisfying the proposed requirements established by the Portuguese motorway contractor BRISA.